In response, security leaders are reevaluating traditional, reactive models of defense. Such legacy approaches often focus on perimeter protection, firewalls, or after-the-fact incident response. However, these methods can leave gaps in visibility and permit data exposure to persist undetected. This blog explores how Data Security Posture Management (DSPM) enables a shift toward proactive data security by continuously monitoring, identifying, and mitigating risks before they escalate into breaches.

The Shift from Reactive to Proactive Security

Reactive security models only respond to incidents after they happen. For example, you patch vulnerabilities after an attack or do forensic analysis after a breach. While important, that’s not enough in today’s world. The rapid adoption of cloud and agile has introduced new data security gaps that traditional tools don’t cover.

As IBM states, the furious adoption of cloud computing, agile cloud-native development and both AI and machine learning led to data security risks and vulnerabilities that these technologies don’t always address, leaving organizations at risk of data breaches. In particular, the movement of data into multiple cloud accounts and development environments can create unmanaged ‘shadow data’ stores that bypass normal security controls. Such copies of sensitive data (for example, for testing or analytics) may lack proper encryption or access controls. When those hidden data stores are outside of IT control they multiply the attack surface. In short, reacting only after an incident occurs is no longer enough. A proactive stance requires tools that go beyond reactive alerts and continuously guard your data.

Understanding DSPM: A Data-First Approach

Data Security Posture Management (DSPM) is a new discipline that puts security back on the data. In simple terms, DSPM is a technology that finds your sensitive data across cloud and hybrid environments, assesses its risk and helps you protect it. In practice, DSPM inverts the traditional security model. Instead of concentrating only on defending systems or networks, it centers on protecting the data assets directly. Gartner has dubbed this a ‘data first’ approach, and indeed DSPM was first recognized in 2022 as part of Gartner’s data security hype cycle. This perspective aligns security efforts around the data that drives business value, as opposed to treating data as an afterthought.

A DSPM solution includes several core capabilities. It continuously discovers and inventories data across on-premises systems, SaaS applications, and multiple cloud providers. It then assesses risks by detecting misconfigurations, excessive access permissions, and insecure storage that could expose sensitive information.

Finally it provides remediation and prevention actions such as enforcing access controls or alerting on anomalies. In other words, DSPM solutions find an organization’s sensitive data, assess its security posture, remediate its vulnerabilities and implement safeguards and monitoring to prevent recurrence of identified vulnerabilities. This continuous loop of discovery, risk analysis and automated protection is in contrast to point in time audits or one off scans.

By having an up to date map of where all sensitive data sits and who can access it, DSPM gives security teams a complete view of data risk. For example, if a misconfigured storage bucket or a forgotten database copy has unencrypted personal data, DSPM tools will alert admins before an attacker finds it. If new data stores appear (e.g. spun up by a dev), a proactive DSPM system will include those in its visibility and classification.

This continuous vigilance is a hallmark of the proactive model. As Palo Alto Networks describes it, DSPM deploys graph-based security architectures with continuous attack path analysis to detect interconnected risk chains before they mature into breaches. In short, DSPM transforms ‘reactive alert fatigue’ into ‘a strategic data-centric defense’ that systematically reduces the cloud attack surface.

Continuous Monitoring and Risk Mitigation

A core aspect of DSPM is continuous monitoring. Rather than performing a one-time scan of data assets, DSPM systems perpetually watch for changes. New data entering the environment is automatically discovered and classified. Existing data is repeatedly analyzed for vulnerabilities. This continuous mode aligns well with modern DevOps practices, where infrastructure and data change rapidly. For example, Rubrik highlights that DSPM can provide near real-time views into data proliferation to reduce sensitive data exposure risk. That means as soon as a new copy of sensitive data appears (a backup or log file), DSPM picks it up. This timeliness gives defenders a window to act before an incident.

Moreover, DSPM can use contextual insights and AI/ML to prioritize what needs attention now. By understanding the relationships between users, data stores and config, DSPM can find combinations of vulnerabilities. For instance, a harmless misconfiguration might become critical if it accidentally grants cloud storage access to all employees. Without DSPM, security teams might never notice this risk in time. But a DSPM solution would flag that attack path and enable pre-emptive remediation. In practice, DSPM platforms often integrate with identity and access management to automatically detect over-entitlements (users who can see more data than they need) and suggest least-privilege fixes.

Mitigation is another pillar of the proactive model. It is not enough to just find risks; DSPM aims to reduce them. Leading DSPM solutions include built-in remediation features. For example, BigID’s announcement of a DSPM service in June 2025 emphasizes ‘Built-In Remediation’ that automatically enforce policies, revoke access, and reduce exposure. In real terms, if a classification scan finds that a set of files contains unprotected personal data, the DSPM tool might automatically enhances the permissions or alert administrators to delete it. This capability moves beyond passive reporting, it proactively closes the gap. By automating policy enforcement, DSPM helps prevent data issues from persisting until a breach occurs.

Industry Innovations in DSPM

The DSPM concept has gained strong traction in the industry, with multiple vendors introducing solutions to operationalize these capabilities. For example, Zscaler announced in May 2024 that it had natively integrated Data Security Posture Management into its cloud data protection platform. Zscaler describes its DSPM as a central part of its data protection offering that discovers, classifies, and protects sensitive data in public clouds such as AWS and Microsoft Azure.

In practice, this means Zscaler’s customers can continuously scan their cloud storage, SaaS applications, and other data repositories for unprotected data and apply AI-driven policies to safeguard it. The company’s executives emphasized that complete data visibility is the first step toward prevention, noting that DSPM helps replace multiple legacy point products and close gaps in data security.

Rubrik, a leader in cloud data protection, also highlighted the role of DSPM in its platform. In December 2024, Rubrik announced that it was adding DSPM capabilities to its Rubrik Security Cloud in order to unblock secure adoption of AI and protect data wherever it resides. Rubrik highlights that DSPM in its platform allows customers to proactively reduce data risks across fragmented cloud, SaaS, and on-premises environments. This means that organizations using Rubrik can activate DSPM to continuously map where their sensitive data is stored, automatically classify it (including by regulatory sensitivity or business importance), and then identify any misconfigurations or risky exposures. Importantly, Rubrik emphasizes ease of deployment; existing customers can enable DSPM within their current environment without lengthy new scans or infrastructure changes. This illustrates how vendors are building DSPM to integrate smoothly into existing security processes rather than requiring disruptive rip-and-replace projects.

BigID, a data intelligence and privacy platform, has likewise focused on making DSPM accessible. In mid-2025, BigID launched a new DSPM offering tailored for managed service providers (MSPs) and mid-market customers. BigID’s announcement highlights that DSPM is a fast-growing category that gives organizations deep visibility into sensitive data, context-rich risk insights, and automated remediation across SaaS, cloud, and hybrid environments. With its DSPM Express program, BigID enables MSPs to deploy enterprise-grade DSPM in hours.

BigID highlights key benefits such as faster time-to-value and built-in automated remediation. For example, the ability to automatically revoke risky permissions when a data exposure is detected. BigID’s CEO explained that as customers ‘double down on securing sensitive data, preparing for regulation, and managing AI risk, MSPs need a smarter way to deliver outcomes’.

Another data security vendor, Varonis, has also extended its DSPM capabilities. In January 2024, Varonis announced an expansion of its DSPM support to include Snowflake, a leading cloud data warehouse platform. This integration means Varonis can now continuously discover and secure sensitive data within Snowflake instances. According to Varonis, its platform continuously discovers and classifies critical data, removes exposures, and detects advanced threats with AI-powered automation. Varonis’s DSPM component is continually scanning the Snowflake data cloud for unsecured sensitive data (for example, exposed financial records or PII). It then alerts or remediates issues (such as enforcing least-privilege permissions or encrypting data). The executive team at Varonis stated that customers could feel confident that Varonis is watching and securing their data no matter where it lives across the cloud. This illustrates how DSPM is being embedded into broader data security platforms to provide ongoing protection across all data stores.

These industry developments show a clear trend. Vendors recognize that simply reacting to breaches is insufficient, and they are building tools to help organizations get ahead of threats. From Zscaler’s AI-powered data protection to Rubrik’s accelerated AI adoption and BigID’s MSP-friendly DSPM highlights a shift toward continuous insight and control over data.

The Impact of Proactive Data Security

Moving from reactive to proactive defense, organizations can improve their data security posture big time. Continuous DSPM monitoring means vulnerabilities are found long before attackers can exploit them. For example, detecting an over-permissioned user or an exposed database credential weeks ahead can avert a potential breach. Proactive DSPM also speeds up compliance. Many regulations require knowing where personal or sensitive data resides and who can access it. DSPM automates this inventory and reporting, reducing the risk of regulatory penalties.

Moreover, by automating remediation, DSPM reduces human error and response time. Where a security team might have to manually review logs or investigate alerts, DSPM platforms can automatically quarantine or encrypt risky data or block exfiltration paths. This efficiency not only lowers risk but can also lower costs. For instance, preventing a breach saves potentially millions compared to the expense of incident response and remediation. It also means less downtime and reputational harm from data loss.

Proactive security gives you confidence in your data driven initiatives. As companies move to the cloud, IoT, and AI they generate and rely on massive volumes of data. DSPM gives you the confidence that data is being protected all the time.

However, implementing DSPM requires a clear understanding of the data estate and possibly integration with many systems (cloud accounts, SaaS apps, on-prem data stores). However, leading DSPM solutions emphasize ease of deployment. For example, Rubrik’s approach allows customers to activate DSPM within existing infrastructure without disruptive new data scans. BigID’s MSP-focused solution provides turn-key deployments for mid-size customers. These developments suggest that the industry is aware of adoption hurdles and is building Data Security Posture Management (DSPM) tools to overcome them.

The post Proactive Data Security: How DSPM Transforms Reactive Security Models appeared first on ITDigest.

For example, recent government figures show that Medicare Fee-for-Service had an estimated 7.66% improper payment rate in FY2024, reflecting widespread billing and coding errors. Moreover, CMS notes that the ‘vast majority’ of these improper payments occur because claims lack sufficient documentation. This finding suggests that many payment errors are avoidable with better data capture and review.

In this environment of mounting cost pressure and regulatory scrutiny, providers face complex payer relationships and economic pressures. So, AI revenue cycle management is now essential for effective billing operations.

Streamlining Billing and Coding

AI technologies can automate routine billing and coding tasks, freeing staff for more complex work. NLP and machine learning can automatically assign diagnosis and procedure codes from clinical documents. This cuts down on manual work and mistakes.

Vendors describe AI-powered billing modules that validate claims before submission. For example, AGS Health offers an AI platform with dedicated modules for revenue cycle automation and computer-assisted coding. Its revenue cycle automation tools aim to optimize billing and collections processes, prevent denials, and automate tedious, time-consuming tasks. Its computer-assisted coding suite is designed to increase coder productivity while reducing denials, missed charges, and low risk scores.

In practice, these tools cross-check documentation against coding rules in real time. Similarly, FinThrive highlights that its agentic AI can flag incomplete documentation and apply real-time coding corrections. Such automation can catch common errors (like missing modifiers or mismatched codes) before claims go out. The result is cleaner claims on first submission, which reduces rework.

AI-driven billing systems make workflows easier. They remove manual data entry and use consistent rules. Automating code assignment and claims checks boosts accuracy. It also lessens staff workload, which increases operational efficiency.

Also Read: Medical Device Cybersecurity 101: Why It’s a Business Risk, Not Just a Tech Issue

Minimizing Claim Denials

Claim denials are a major revenue risk for providers. Each denial often requires costly appeals or corrections, delaying payment. AI can help minimize denials in two ways: by preventing errors that lead to denials, and by quickly identifying denial trends for corrective action. Advanced analytics and automation tools can detect patterns that typically trigger rejections. In June 2025, FinThrive introduced a Denials and Underpayments Analyzer powered by AI. This tool takes raw payer data and turns it into actionable insights, pinpointing denial patterns, underpayment trends, and high-value recovery opportunities. It flags which services or payer relationships are most likely to be underpaid or denied so that staff can focus appeals efforts where they matter most.

Leading RCM firms emphasize that AI-led denials management improves financial outcomes. R1 states that automating tasks such as coding, billing, and denials management increases efficiency. It also enhances accuracy and improves cash flow. These abilities lead to fewer rejected claims and more revenue collected. As denials drop, providers capture more of the revenue they bill and avoid the administrative burden of lengthy appeals.

Accelerating Reimbursements

Fewer errors and denials directly translate into faster payments. When claims are accurate on the first try, payers process them without delay. AI also helps prioritize collections by predicting which accounts are most likely to pay quickly. Industry sources describe dramatic gains in throughput. R1 notes that its AI initiatives will unlock faster, more precise, scalable reimbursement outcomes.

This means providers receive payments sooner and more reliably. FinThrive similarly emphasizes faster revenue flow; its agentic AI capabilities enable providers to recover revenue faster, reduce operational friction, and adapt to payer behavior in real time. By embedding intelligence across the revenue lifecycle, the system helps organizations operate more efficiently [and] recover revenue faster.

In other words, digital assistants can automatically route the right claims, follow up on unpaid accounts, and expedite routine tasks like eligibility checks and prior authorization. Even small percentage improvements in days in accounts receivable can significantly boost cash flow. While specific results vary, vendors claim that clients see measurable performance gains from these technologies. In sum, by reducing the cycle time on each claim and account, AI accelerates overall revenue collection and improves cash flow predictability for healthcare systems.

Enhancing Financial Performance

The cumulative effect of AI in RCM is stronger financial performance. By eliminating waste and capturing revenue more effectively, healthcare organizations improve net margins and free up resources for care delivery. For example, FinThrive reports that its technology helps healthcare organizations increase revenue, reduce costs, [and] expand cash collections across the revenue cycle. The platform aims to capture revenue that might otherwise be lost due to denials or underpayments. It also helps reduce administrative costs.

These improvements align with regulatory goals as well. The U.S. Centers for Medicare & Medicaid Services (CMS) has reported that billions are lost annually to improper payments (e.g. US$ 31.7 billion in Medicare FFS for 2024), much of which stems from documentation gaps. By integrating AI into RCM, providers help ensure compliance with coding and billing requirements, thereby reducing waste.

Overall, industry leaders characterize AI-augmented RCM as a transformative investment. R1’s CEO envisions an ‘AI-native’ revenue cycle that delivers a ‘faster, frictionless, and more transparent financial experience’ for providers and patients. Cognizant, in its TriZetto business, is embedding AI to offload tasks like claims adjudication and prior authorization so teams can focus on patient care.

These technologies close the gap between services rendered and payment received. When denials are minimized and approvals accelerated, organizations need fewer write-offs or bad-debt provisions. Executives can then redirect budget to clinical priorities rather than chasing paperwork.

AI revenue cycle management has the potential to raise net revenue and lower costs simultaneously. For healthcare leaders facing thin margins and evolving payer rules, these tools offer measurable gains.

The post AI Revenue Cycle Management: A Complete Guide for Healthcare Leaders appeared first on ITDigest.

AI in telecom is no longer confined to labs or pilot projects. It’s across the telecom value chain, from network optimization to personalized customer experiences. For operators, AI is not just about efficiency. It’s a strategic tool for competitiveness, resilience and growth.

Here are the top 5 AI applications in telecom you should know in 2025, each shaping the industry in its own way.

1. Network Optimization That Includes Smarter, Self-Healing Systems

Telecom networks are the lifeblood of digital economies but they are also getting more complex. Rising data traffic, distributed devices and cloud driven services require constant adaptation. AI driven network optimization allows providers to move beyond static configurations to dynamic, self adjusting systems.

AI algorithms monitor real time traffic patterns, identify congestion risks and automatically reroute data flows. This means more efficient bandwidth usage and less service disruptions. In 2025 many networks will move to fully autonomous operations where machine learning models predict performance bottlenecks before they happen and deploy fixes instantly.

Another aspect of AI enabled optimization is energy efficiency. Telecom infrastructure consumes massive volume of power. By using AI, operators can fine tune usage during peak and off peak times, reducing costs and environmental impact. For leaders in the industry, this means better uptime, more reliable service quality and a direct contribution to sustainability goals.

The move from reactive to proactive network management is perhaps the most transformative shift. Instead of waiting for failures, networks can now heal themselves. This keeps customers connected and reduces the operational burden on human engineers.

In March 2025, at Mobile World Congress 2025, Jio Platforms, together with Nokia, AMD, and Cisco unveiled the Open Telecom AI Platform, a groundbreaking, multi-domain AI orchestration layer built with open APIs and model-agnostic architecture. It integrates agentic AI, LLMs, and telecom-specific smaller models to enable self-optimizing, secure, and monetizable networks.

2. Fraud Detection Focuses on Strengthening Security in Real Time

Fraud is one of the biggest challenges for telecom operators. From SIM card cloning to subscription fraud and international call scams, fraudsters are always looking for new vulnerabilities. Traditional fraud detection methods rely heavily on static rules and after the fact investigation. AI changes that equation entirely.

AI powered fraud detection systems analyze call records, payment data, and user behavior in real time. They flag anomalies that deviate from typical usage patterns and alert before large scale losses occur. By learning from historical data and live traffic, these systems evolve with the threat landscape.

In 2025 telecom companies are embedding AI into their fraud management frameworks as the first line of defense. The advantage is speed and precision. Instead of broad suspicion that inconveniences customers, AI can pinpoint suspicious activity at the micro level while allowing legitimate transactions to go through smoothly.

Beyond financial fraud, AI is also used in cybersecurity. Telecom networks are the backbone for government agencies, enterprises and millions of consumers. Breaches can spread across entire ecosystems. AI systems that detect intrusions, monitor abnormal access attempts and neutralize threats in milliseconds are becoming the norm across the industry.

For revenue leaders, fraud prevention is more than a cost control measure. It builds trust. Customers who know their data and services are secure are far more likely to stay loyal, so AI driven security is a competitive differentiator as much as a safeguard.

In June 2025, Subex introduced FraudZap, a lightweight, AI-driven fraud detection platform tailored for telecom operators, debuting with a handset fraud use case. It targets patterns such as fake identity submissions, reseller collusion, device flipping, and similar threats, all in real time.

Also Read: The Role of Fixed Wireless Access in 5G Network Expansion

3. Customer Service is Where AI Acts as the New Frontline

Telecom providers serve millions of customers which creates enormous demand for support services. Questions range from billing to technical troubleshooting and expectations for resolution are higher than ever. AI powered customer service solutions are stepping up as the new frontline in 2025.

Virtual assistants and chatbots use natural language processing to tackle complex questions. They can solve problems and even predict what customers need. Today’s AI agents learn from every interaction. Unlike earlier chatbots, which gave scripted responses, these agents become more accurate and human-like over time.

AI doesn’t work in isolation. It augments human support teams by providing real time suggestions, recommended scripts and knowledge base insights. This reduces handling times, improves first call resolution rates and increases customer satisfaction. Telecom companies are also using AI to personalize. By analyzing customer profiles, usage history and service preferences, AI can recommend the best plans, upgrades or troubleshooting steps for each user.

What sets AI driven customer service apart in 2025 is integration. Service channels are no longer fragmented. Whether a customer reaches out via call center, mobile app or social media, AI ensures consistency and continuity. The result is not just faster support but a whole new standard of experience, proactive, seamless and customer first.

4. Predictive Maintenance is Anticipating Failures Before They Happen

Telecom infrastructure is massive – cell towers, fiber cables, base stations, and routers. Maintaining this network is a costly and complex task. Traditionally providers relied on scheduled maintenance or reacted only when issues arose. AI driven predictive maintenance has changed this model entirely.

With predictive systems, sensors embedded across the network feed performance data into AI models. These models detect early signs of equipment failure, whether it’s overheating, unusual vibration or fluctuating power levels. Instead of waiting for a failure to happen, operators can act before it does. This proactive approach reduces downtime, reduces repair costs and extends the life of critical assets. Most importantly it ensures service continuity. In an era where customers expect seamless connectivity, even short outages can damage brand and revenue. AI driven predictive maintenance keeps telecom companies’ networks stable while optimizing resource allocation.

For field engineers AI adds efficiency. Maintenance teams can be sent with exact knowledge of what to fix and where, reducing wasted trips. In large networks of thousands of sites, this precision means big operational savings and better service reliability.

5. Personalized Offerings is Redefining Customer Engagement

Telecom providers no longer compete on connectivity alone. In 2025, differentiation comes from personalized offerings that match individual customer needs. AI is at the heart of this shift.

By analyzing customer usage patterns, preferences and historical interactions, AI models generate super personalized recommendations. From suggesting the most cost effective data plans to offering premium services that match lifestyle habits. Enterprise customers benefit too, with AI recommending bundled solutions, cybersecurity add-ons or IoT services based on business requirements.

Personalization goes beyond sales. AI can predict when customers will churn and offer targeted retention offers. It can power contextual engagement like adjusting roaming plans when customers travel or suggesting upgrades as devices age. Being able to offer the right thing at the right time builds stronger relationships and new revenue streams.

For B2B customers, personalization is particularly powerful. Large organizations need flexible contracts, dynamic bandwidth allocation or tailored security features. AI driven insights allow telecom operators to serve these needs with precision and become trusted partners rather than just service providers.

Why These Applications Matter for Telecom Leaders

The five applications of AI, network optimization, fraud detection, customer service, predictive maintenance and personalized offerings, are more than individual use cases. Together they represent a strategic shift in how telecom companies operate, compete, and grow.

For revenue leaders AI in telecom is not just a technology upgrade. It’s a business model enabler. Smarter networks reduce costs and improve customer satisfaction. Advanced fraud detection builds trust in the ecosystem. Predictive maintenance ensures continuity and operational efficiency. Personalized offers build customer loyalty and upsell. And AI powered customer service creates scalable positive experiences that keep customers engaged.

The real opportunity is in integration. When these AI applications work together they create a whole system where networks run better, customers feel more valued and business outcomes align to long term growth. Telecoms that treat AI as a strategic asset not a set of tools will be the winners in 2025 and beyond.

The post Top 5 Applications of AI in Telecom You Should Know in 2025 appeared first on ITDigest.

Recent advancements in this field have revolutionized a variety of applications, from those that track traffic congestion and provide real-time alerts to those that evaluate consumer flow in stores to optimize sales, in addition to more well-known applications like facial recognition and smart parking.

How does this technology operate, and how can it help your business? It looks great to reinvent security and operational insight.

The fundamentals of video analytics and how it is transforming security and operational insight for CIOs are covered in this article.

Let’s dig in!

What is Video Analytics?

Video analytics is a cutting-edge technology that automatically examines video footage. Real-time processing of video data by intelligent algorithms produces insights about the events depicted in a sequence of photos. Detecting and learning about the movement of objects, people, and vehicles in CCTV data is a popular application of video analytics for security.

A better and more efficient way to review and view security footage is through video analytics surveillance systems. Multiple cameras’ worth of footage over several days can be auto-filtered by topics of interest, and security staff can spot and react to suspicious activity.

How Does Video Analytics Work?

Here is a broad overview of how a video analytics solution operates. The scheme is always the same; however, the architecture of a solution might change depending on the specific use case.

There are two methods for analyzing video content: either in real time, by setting up the system to send out alerts for particular incidents and events that happen right then, or in post-processing, by carrying out sophisticated searches to make forensic analysis chores easier.

Feeding the System

Several streaming video sources may provide the data for analysis. Online video streams, traffic cameras, and CCTV cameras are the most popular. Nonetheless, the solution can typically incorporate any video source that employs the relevant protocol (such as HTTP or RTSP, or real-time streaming protocol).

Coverage is a crucial objective; one must be able to see the entire region and the potential locations of the events under observation from a variety of perspectives. Keep in mind that, as long as it can be handled, more data is preferable.

Edge Processing vs Central Processing

Central processing refers to the ability to run video analysis software centrally on servers that are often found in the monitoring station. Another option is to incorporate it within the cameras themselves, which is referred to as edge processing.

When building a solution, the camera selection should be considered. Many old software programs were written with central processing power only. But hybrid systems are becoming more common these days. However, it’s always good to focus on forensic analysis on the central server and real-time processing on cameras whenever possible.

By using a hybrid approach, the cameras process less data for the central servers to handle, which would normally require a lot of processing power and bandwidth as the number of cameras increases. And the software can be set up to only send information about suspicious events to the server through the network, which reduces network traffic and storage requirements.

In the meantime, centralizing the data for forensic analysis makes it possible to apply a variety of search and analysis methods, ranging from ad-hoc implementations to generic algorithms, each of which uses a unique set of parameters to assist in balancing the noise and silence in the findings. In essence, you can use your own algorithms to achieve the intended outcomes, making this a particularly adaptable and alluring plan.

Establishing Training Models and Scenarios

Following the planning and installation of the physical architecture, you must specify the scenarios you wish to concentrate on and train the models that will identify the desired occurrences.

Car crashes? Flow of the crowd? Can a retail establishment use facial recognition to identify known shoplifters? Every situation results in a set of fundamental tasks that the system has to be able to complete.

As an illustration, identify vehicles, eventually identify their type (e.g., truck, car, or motorbike), follow their route frame by frame, and then examine how those pathways change over time to identify potential collisions.

Also Read: AI SaaS Explained: How Artificial Intelligence is Transforming Software Development 

Why CIOs Are Using Video Analytics for Better Insights

Video analytics is changing security and operational insight for CIOs by turning footage into intelligence. Advanced AI-driven tools now go beyond monitoring to detect anomalies, track behavior patterns, and predict risks in real time. This means faster threat response and valuable insight into operations, workforce efficiency, customer behavior, and space utilization. For CIOs, it means using existing video infrastructure as a strategic asset, safer and better decision making without extra cost.

At the same time, video analytics is helping CIOs align technology with business goals. By integrating video data with enterprise systems, organizations can optimize resource allocation, improve compliance, and strengthen operational resilience. From reducing downtime in industrial environments to enhancing customer experience in retail, these insights allow CIOs to measure outcomes, making video analytics a key enabler of security and digital transformation.

Industry Applications

Smart Cities / Transportation

The development of smart cities has benefited greatly from the application of video analytics in the transportation sector.

If proper traffic management measures are not implemented, a rise in traffic, particularly in urban areas, may lead to an increase in accidents and traffic congestion. In this situation, intelligent video analysis tools can be quite helpful. Traffic is a big problem in urban areas, wasting time, money, and lives. According to the U.S. Government Accountability Office, congestion is going to get worse, so we need intelligent transportation systems that use video analytics to monitor and manage traffic.

Traffic analysis can be used to track traffic congestion and make dynamic adjustments to traffic signal control systems. Real-time detection of risky situations, including a car stopped in an unlawful area on the highway, a driver traveling in the wrong direction, a car moving strangely, or a car that has been in an accident, can also be helpful. For example, the U.S. Department of Transportation deployed AI-powered Adaptive Signal Control Technology in 8 Florida cities and reduced travel time by 9.36% across several corridors. These systems are useful for gathering evidence for a lawsuit in the event of an accident.

The city of New York provides an excellent illustration of how video analytics can be applied to tackle practical issues. The New York City Department of Transportation employed machine learning and video analytics to identify parking infractions, traffic congestion, weather trends, and other significant traffic incidents. The cameras record the events, analyze them, and notify city officials in real time.

Security

Video surveillance is an old task in the security domain. However, from the time that systems were monitored exclusively by humans to current solutions based on video analytics, much water has passed under the bridge.

Facial and license plate recognition (LPR) techniques can be used to identify people and vehicles in real-time and make appropriate decisions. For instance, it’s possible to search for a suspect both in real-time and in stored video footage, or to recognize authorized personnel and grant access to a secured facility.

Another essential security system function is crowd management. In locations like malls, hospitals, stadiums, and airports, advanced video analysis systems can have a significant impact. When a threshold is achieved or exceeded, these systems can send out notifications and provide an estimated crowd count in real time. In order to identify movement in undesirable or forbidden directions, they can also examine the flow of the crowd.

One of the main benefits of these methods is that video content analysis systems may be trained to identify particular occurrences, often with a high level of complexity. Identifying flames as soon as feasible is one example. Or, in the case of airports, to sound an alert if someone walks in the wrong direction or enters a prohibited area. The real-time detection of unsecured luggage in a public area is another excellent use case.

Algorithms that can filter out motion from wind, rain, snow, or animals allow for the robust execution of traditional tasks like intruder detection.

In the field of security, the capabilities provided by intelligent video analysis are expanding daily, and this trend is expected to continue.

Concluding Thoughts

Video analytics tools are incredibly helpful in our day-to-day activities. This technology can be applied to a wide range of industries, particularly as the complexity of possible uses has increased recently.

From smart cities to airport and hospital security measures to retail and shopping center personnel tracking, the field of video analytics makes operations more efficient and less time-consuming for people while also saving financial resources for companies.

The post How Video Analytics Is Reinventing Security and Operational Insight for CIOs appeared first on ITDigest.

For this reason, bot management is essential for any online business. Investing in strong bot mitigation software is not just a good to have, but also essential, given that sophisticated bots are now imitating human behavior using AI and sophisticated fingerprinting evasion tactics.

This article will discuss the idea of managing bots, its significance, and how a strong management system may assist you in managing bot traffic and safeguarding the security of your website, mobile application, and API.

What is Bot Management?

The process of recognizing every single bot on your network and comprehending its objectives so that you can react appropriately is known as bot management. To rank in search engine results, you should grant access to your website to a helpful bot, such as an SEO tool or Googlebot and Bingbot.

Malicious bots should be banned right away, such as those that aim to steal your content or prevent actual visitors from visiting your website. Both identifying bot activity and figuring out whether it is hostile or not are part of bot management.

Two major obstacles in managing bots are:

• distinguishing between bot traffic and actual human traffic
• distinguishing between harmful bots (bad bots) and those with good intentions (good bots)

Because modern bots are so complex, it can be difficult to detect them. Without the proper bot management tools, it might be difficult to distinguish between human users and bots because they can imitate human behaviors like randomized clicks and nonlinear mouse motions.

And keep in mind that not all bots are harmful. Some bots are useful for your website; for instance, Googlebot crawls and indexes websites so that users can find them on Google. You should prevent malicious bots when they show up, but you shouldn’t block Googlebot if you want your website to be shown on Google.

Therefore, there are two components to an optimal bot management practice:

• Effective identification of bad bot traffic
• Management and mitigation of malicious bot traffic

What is the Role of a Bot Manager?

Any software program that controls bots is called a bot manager. Rather than merely barring all non-human traffic, bot managers ought to be able to let some bots through while blocking others. A page cannot appear in Google search results if all bots are prohibited and Google bots are unable to index it, for example. This will significantly lower the amount of organic traffic that the website receives.

A good bot manager achieves the following objectives. It is capable of:

• Recognize human visitors from bots.
• Determine the reputation of the bot
• Determine the IP addresses of bot origins and block them according to their reputation.
• Examine the behavior of the bots and add ‘good’ ones to allowlists.
• Use JavaScript injection, the CAPTCHA test, or other techniques to test possible bots.
• Limit the rate at which any possible bot overuses a service.
• Deny ‘bad’ bots access to specific resources or content.
• Provide bots with alternate content.

Good Bots vs. Bad Bots

The roles and goals of good and bad bots differ. Good bots can enhance customer experiences and are made to assist consumers and businesses. To assist customers find the best deals, search engine bots, for instance, trawl the web and index material so that it appears in searches. By stealing data, taking over user accounts, submitting spam data through online forms, and engaging in other malicious activities, bad bots, on the other hand, are employed to carry out evil aims and can seriously affect people and businesses.

Of all web traffic, 42% is made up of bots, 65% of which are malicious. Generally, two key characteristics set good bots apart from bad ones: they don’t conceal their status as bots, and they adhere to the guidelines specified in a website’s ‘robots.txt’ file. Usually, they are used by respectable, well-known businesses that offer beneficial services. Although they are known to attempt to pass themselves off as genuine bots, bad bots do not clearly identify themselves as good bots do, nor do they adhere to webmasters’ regulations for bots.

Importance of Bot Management

According to Security Magazine, bot management solutions that are combined with web application and API protection (WAAP) can provide comprehensive protection. As bots get more complex, effective bot management systems may help safeguard both users and websites. Over 65% of websites are still vulnerable to simple bot attacks. What bot management should offer is as follows:

• Good solution criterion: Bot management must operate in the background to eliminate malicious bots and boost the efficiency of beneficial ones.
• Effectiveness: Even with bots that may evade common detection techniques, a bot manager must be able to distinguish between good and bad bots, block harmful bots, and do so.
• Efficiency: Using real-time scrubbing techniques, bots must be screened through several stages while maintaining a high degree of performance and speed for the user.
• Detection ability: Because bots can frequently imitate human behavior, bot administrators must be able to identify harmful bots, no matter how complex they are.
• Control: Bot managers must have the ability to both identify and manage which bots are good and bad. For instance, giving a hostile bot fake data back can both counteract its bad intent and provide a counterattack.

How is Bot Management Carried Out?

Bot managers can employ CAPTCHA or JavaScript challenges to detect bots, which detect whether a conventional web browser is being used. By comparing a user’s activity to the typical behavior of users in the past, they can also utilize behavioral analysis to identify which users are bots and which are humans. The latter requires bot administrators to have a vast amount of high-quality behavioral data to compare to.

If a bot is found to be malicious, it may be prevented from using an online resource entirely or redirected to another page.

An allowlist, which is the reverse of a blocklist, is a list of permitted bots to which good bots can be added. Through additional behavioral research, a bot manager can also differentiate between good and bad bots.

Using the robots.txt file to create a honeypot is another method of managing bots. A honeypot is a fictitious target that reveals the bad actor as malevolent when they gain access. In the case of a bot, a honeypot can be a page on the website that the robots.txt file prohibits bots from accessing. While some evil bots will crawl the webpage, good bots will scan the robots.txt file and avoid that webpage. Bad bots can be found and stopped by monitoring the IP address of the bots that enter the honeypot.

Which Kind of Bot Attacks Are Prevented by Bot Management?

Numerous attacks can be prevented with the use of a bot management solution:

• DoS attacks
• DDoS attacks
• Credit card stuffing
• Credential stuffing
• Spam content
• Brute force password cracking
• Data scraping/web scraping
• Email address harvesting
• Click fraud
• Ad fraud

Even though these additional bot activities aren’t generally seen as ‘malicious,’ a bot management need to be able to prevent them:

• Inventory hoarding
• Shopping cart stuffing
• Automated posting on social forums or platforms

Summing it Up

Bots represent an obvious and increasing threat to any digital business as they continue to advance in speed, sophistication, and deceit. Traditional protections are falling behind, and it’s becoming harder to distinguish between both good and bad bots. Bot management is a strategic necessity for CIOs and CISOs, not just a technical precaution.

Organizations may improve security while maintaining user experience by investing in intelligent bot management solutions that can identify intent, differentiate malicious automation from helpful traffic, and adjust in real time. More significantly, proactive bot defense contributes to the protection of brand reputation, digital assets, and revenue.

The question is not whether to implement bot management in the current climate, where almost all businesses rely on online platforms, but rather how soon to do so. Leaders who take immediate action will protect their businesses and create a sustainable competitive advantage.

The post The Bot Management Playbook for CIOs and CISOs appeared first on ITDigest.

Imagine now that you have a smart assistant that is always available. It expedites your assistance, provides useful insights, and aids in understanding the needs of your users. It makes it simpler to find and address bugs. Additionally, it improves your marketing efforts without putting your personnel through more work.

You seem to have won the lottery. But in reality, it’s AI SaaS’s strength. 70% of SaaS businesses are incorporating AI and machine learning into their solutions, claims Gitnux. Even better, 78% of them stated that their ROI was positive in the first year. It isn’t simply hype. That is a tangible, quantifiable effect of generative AI techniques.

You have a lot of questions still, no doubt. Let’s discuss the reasons why businesses use AI in SaaS in this post, along with the advantages of using this approach.

Understanding AI SaaS

Let’s briefly describe SaaS and AI before describing SaaS AI. Then, we’ll examine what happens when they are combined.

Using software online rather than installing it on your device is known as software as a service, or SaaS. It’s similar to streaming a film rather than purchasing a DVD. All you need to do is pay a subscription fee and log in using your browser to get started. SaaS is adaptable, simple to scale, and provides businesses with consistent revenue over time.

Computers can learn to think like people thanks to artificial intelligence, or AI. It enables self-improvement, problem-solving, and learning in machines. Everyday tools like chatbots, virtual assistants, and image-recognition software are powered by machine learning. Thus, your technology is getting more intelligent and practical.

SaaS software gets significantly smarter when AI is incorporated. AI SaaS automates processes, gathers insights from data, and customizes your experience. In a nutshell, AI systems speed up and improve SaaS applications.

Also Read: Training AI Locally: The Rise of Sovereign AI Infrastructure 

The Top 5 Advantages of SaaS AI

Now that you are aware of the market conditions, you are likely interested in what AI in SaaS may provide. Here is a list of advantages of AI SaaS:

1.     Customization

The needs of each of your clients are distinct. Serving each of them in a way that feels intimate is the difficult part.

AI tools enable this. Whether it’s making playlist recommendations in a streaming service or proposing meals in a food delivery app, they assist SaaS platforms in analyzing user behavior and preferences to provide personalized experiences. The outcome of SaaS for AI? Better retention, happier users, and all of this is automated by machine learning.

2.   24/7 chatbot client service

Undoubtedly, an influx of client inquiries might overwhelm your staff. It’s simply not possible to respond to everyone quickly enough at times. As a result, some clients become frustrated or perplexed.

Chatbots with AI capabilities can quickly respond to frequently asked questions and resolve typical problems with SaaS applications. You don’t have to wait. This keeps your consumers satisfied and gives your team more time to deal with the difficult situations.

3.   Predictive Engagement

AI algorithms can identify the warning indicators early on if people cease using your app. You may take action before people leave by using predictive analytics to determine why they lose interest.

To get them back, you can give them a discount, a useful suggestion, or a customized message. You can increase retention and sustain user engagement using AI SaaS.

4.   Data-driven, more intelligent marketing

AI SaaS improves the intelligence of your marketing. AI takes into account what your clients do rather than speculating about what might work. It locates possible customers by classifying them according to user behavior.

It also enables you to make effective use of your advertising budget and displays the most important content. Better outcomes, more sales, and more income will follow.

5.    AI-driven suggestions

AI functions within your product as a smart assistant. It leads users through the following stages after learning how they interact with your platform. From advanced features to onboarding advice, AI makes sure users get the most out of your tool.

This type of instruction facilitates the use of your goods. It clears up misunderstandings and motivates users to experiment with more functionalities. Better user experience, more engagement, and more robust product uptake are the outcomes.

Thus, AI SaaS is a wise business solution that offers numerous advantages. If you want to increase customer traffic and improve the value of your product, implementing AI will be your best bet.

Difficulties with AI SaaS

AI in SaaS has many uses and advantages, but it can also hinder companies. The primary cause? A lot of businesses aren’t really ready for the difficulties that come with putting artificial intelligence into practice. What potential roadblocks can hinder your SaaS firm, and how can you get around them?

Risk is one of the main issues. When SaaS firms include AI into their systems, they frequently have no idea what problems they would encounter. Sometimes it’s unclear how it comes to certain conclusions. Particularly in high-stakes sectors like finance or transportation, this lack of openness can be dangerous.

In order to handle this, strive for clear, explicable AI models that facilitate comprehension and increase confidence in the results.

The issue of bias in AI models presents another difficulty. Your AI may choose biased courses of action if it is trained on biased data. Unfair results can hurt actual people and ruin your brand, which is a major issue in sectors like finance and insurance.

Fix? Test your models for bias on a regular basis and retrain them using more accurate and varied data. Make sure your AI development approach incorporates fairness.

Another important consideration is data privacy, particularly when working with sensitive data.

Maintaining user trust requires protecting personal information. Make sure you abide by all privacy regulations and provide a clear explanation of how you gather, store, and use data in order to accomplish this. To protect the data, use robust encryption and anonymization techniques.

Future of AI SaaS

As more and more businesses use artificial intelligence, the AI SaaS industry will have a very promising future. AI is being used by well-known companies like Oracle and Microsoft to reach more consumers and target the right individuals at the right time.

Random product recommendations are no longer effective. AI and predictive analytics are used by businesses such as Amazon to know what customers want before they even search for it. In actuality, the competition is intense, and you run the risk of slipping behind if you don’t stay up.

AI enables you to provide the tailored experiences that today’s users want. AI integration into your SaaS is now required if you want to expand and remain relevant. It’s the right thing to do for your company. Numerous AI SaaS businesses are already adjusting to this change. According to a Tech Jury poll, 42% of SaaS companies are preparing for AI SaaS projects in the near future, while 35% of SaaS companies have already deployed AI.

Final Thoughts

AI SaaS is transforming the way businesses operate. It brings speed, efficiency, and smarter decision-making into everyday tools. Automating processes, improving customer experiences, and enabling better insights help companies stay ahead in a competitive market. While challenges like data privacy and integration exist, they can be managed with the right strategies. Businesses that embrace AI SaaS today will be better prepared for the future, while those that delay risk falling behind.

The post AI SaaS Explained: How Artificial Intelligence is Transforming Software Development appeared first on ITDigest.

An AI data lake is a big storage repository that supports AI and machine learning workloads. It lets raw data in, organized and accessed by analytics. Unlike traditional data warehouses that require schema definition up front, data lakes store data as is. This means data scientists and AI models can access more inputs, experiment fast and find patterns that traditional systems miss.

For CIOs, IT leaders and data folks, knowing how to build and manage an AI data lake is table stakes. It’s not just about storage. It’s about building a data foundation that supports autonomous decision making, predictive modeling and next-gen digital services.

From Data Warehouses to AI Data Lakes

Data warehouses have been the standard for business intelligence. They organize data into structured tables, optimize for queries and work well when data formats are known and consistent. But modern use cases require more flexibility. Companies now deal with streaming data, social media inputs, image files and logs. AI systems need this variety to train accurate models.

The concept of a data lake was born to handle this complexity. It provides a central pool where data is stored in raw format until it’s needed. This reduces the time and cost of preparation. It also makes it easier to run exploratory analysis or train AI models that need access to full datasets without transformation bias.

What distinguishes an AI data lake from a basic data lake is the integration of machine learning tools, metadata layers and governance frameworks tailored to AI workflows. These add-ons allow teams to go from data ingestion to insight faster. They also improve traceability and model performance monitoring. In this sense an AI data lake is not just a repository. It’s a launch pad for intelligent applications.

Key Benefits for Modern Businesses

The benefits of AI data lakes go beyond storage. They offer strategic capabilities that support innovation, efficiency and agility. One of the main benefits is scalability. AI data lakes are often built on cloud-native infrastructure which makes them elastic. As data grows the system expands without performance loss.

According to a 2024 US Department of Energy report, data centers consumed 4.4% of total US electricity in 2023. That’s up from 58 TWh in 2014 to 176 TWh in 2023. By 2028, data center consumption will be between 6.7% and 12% of national electricity use. That’s 325-580 TWh. Most of that growth is due to AI processing demands, so we need efficient, scalable architectures like AI data lakes.

Companies have more data than ever. Data from applications, sensors, users, transactions and third-party systems. Traditional databases and warehouses can’t handle the volume, velocity and variety. AI data lakes are the answer. They allow you to store, manage and analyze large amounts of structured and unstructured data in one place.

A data lake is a scalable storage repository for AI and machine learning. It ingests raw data, organizes and allows advanced analytics to access it. Unlike traditional data warehouses that require schema definition upfront, data lakes store data as is. This allows data scientists and AI to access more inputs, experiment fast and find patterns that traditional systems miss.

For CIOs, IT leaders and data professionals, understanding how to build and manage an AI data lake is becoming essential. It’s not just about storage. It’s about building a data foundation that supports autonomous decision making, predictive modeling and next-gen digital services.

Also Read: How Distributed Databases Enhance Data Availability and Reliability 

From Data Warehouses to AI Data Lakes

Data warehouses have been the standard for business intelligence. They organize data into structured tables, optimize for queries and work well when data formats are known and consistent. But modern use cases require more flexibility. Companies now deal with streaming data, social media inputs, image files and logs. AI systems need this variety to train accurate models.

The concept of a data lake was born to handle this complexity. It provides a central pool where data is stored in raw format until it’s needed. This reduces the time and cost of preparation. It also makes it easier to run exploratory analysis or train AI models that need access to full datasets without transformation bias.

Key Benefits for Modern Businesses

The benefits of AI data lakes go beyond storage. They offer strategic capabilities that support innovation, efficiency and agility. One of the main benefits is scalability. AI data lakes are often built on cloud-native infrastructure, which makes them elastic. As data grows, the system expands without performance loss.

Security and governance is the foundation. With big data and many users, policies must control who can see what. Role-based access, encryption and audit logs are standard. Compliance to industry standards is key especially in regulated industries.

The last layer is integration. AI data lakes should connect easily to analytics platforms, modeling tools and visualization dashboards. This interoperability ensures data flows across systems without duplication or friction.

Deployment Strategies for AI Data Lakes

Choosing the right deployment model for an AI data lake depends on your infrastructure, compliance requirements and data maturity. Many organizations start with cloud-native platforms. These offer rapid scalability, integrated services and cost transparency. Public cloud vendors provide tools for storage, processing and machine learning that are pre-integrated with their data lake offerings.

For organizations with strict data residency or security requirements, hybrid deployment is an option. Sensitive data stays on premise, non-sensitive workloads run in the cloud. This gives you flexibility without sacrificing control. Hybrid deployments are used in industries like finance, healthcare and defense where compliance is tight.

On-premise data lakes suit high-security setups or limited cloud access. They need more investment in hardware and upkeep. These setups offer full control but need planning and skilled staff. No matter the model, portability should stay a priority.

Whatever the model, organizations should focus on portability. Vendor lock-in can limit innovation. Open formats, containerized tools and API-based architectures help keep things modular and adaptable.

In April 2025, Huawei launched its AI Data Lake Solution at the IDI Forum in Munich. This platform integrates storage, metadata and AI pipelines into one system. The goal is to accelerate AI training and inference across industries by simplifying ingestion, scaling and governance. It’s a real-world example of how vendors are operationalizing AI data lakes for enterprise use.

Data Quality and Lifecycle Management

The value of an AI data lake is in its content. Storing large amounts of data without context or validation can lead to noise and bias. Data lakes without oversight become data swamps. To prevent this, governance must be embedded from the start. Lifecycle management helps with cost and usability. Not all data needs to live forever. Archiving old files, deleting redundant entries and tagging high-value assets keeps the data lake lean and relevant. Tiered storage also helps with efficiency. Frequently used files are kept in fast-access tiers, historical data is moved to lower-cost options.

Data validation pipelines catch anomalies, missing fields or formatting issues early in the process. This reduces errors in downstream AI models. Standardizing how data is labeled and categorized helps with consistency across departments. This is especially important in organizations where multiple teams touch the same data lake.

Data engineers, scientists and business teams need to collaborate. A centralized stewardship model helps manage data better. Automated quality checks add another layer of control. Together, they ensure data stays accurate and models work well.

Common Mistakes to Avoid

Many AI data lake projects fail because they miss the foundation. One common mistake is to treat the data lake as a dumping ground. Without structure and planning, the system becomes bloated and unusable. Every data entry should have a purpose, owner and expiration plan.

You’re underestimating metadata. Data without context is useless. Tagging files, recording lineage and adding schema hints saves time and reduces duplication.

Not aligning data lake investments with business goals is another risk. Technology for its own sake rarely delivers ROI. A good AI data lake strategy starts with clear questions. What decisions will the data support? Which processes can be improved? Who will use the insights? These answers guide architecture and prioritization.

Overreliance on a single team is also a problem. AI data lakes are cross functional assets. They work best when IT, analytics and business teams collaborate. Siloed ownership leads to missed opportunities and misaligned priorities.

Best Practices for CIOs and Data Leaders

For tech executives the success of an AI data lake project hinges on leadership and execution. Start with a strong business case. Connect data availability to outcomes that matter, such as faster product delivery, better customer service or reduced risk.

Invest in scalable infrastructure. Plan for exponential data growth, not just today’s needs. Choose tools and frameworks that support current and future workloads.

Define a clear governance model. Define roles, policies and review processes. Encourage transparency on how data is sourced, labelled and used. This builds a culture of accountability.

Collaborate. Cross functional working groups can priorities high value use cases, troubleshoot issues and share insights. Don’t treat the data lake as an IT only responsibility. When business teams see the value, adoption grows faster. Make data literacy a must. Train on querying, visualization and model building tools. Let more people use the data lake without relying on specialists. This opens up access and speeds up decision making.

And measure. How often is the data lake used? How many models does it support? What business outcomes do those models drive. These metrics show value and justify investment.

Final Thoughts for AI-Driven Companies

An AI data lake is more than a store. It’s a platform that connects raw data to smart action. When done well it supports many use cases, reduces complexity and enables learning across the business.

The journey to a good AI data lake is more than just tools. It requires planning, governance, collaboration and vision. CIOs and data leaders who get it right will unlock new capabilities across every line of business.

As data grows and AI becomes part of the daily grind, companies that build a strong data lake foundation will win. They will make faster decisions, respond to change better and create experiences that are personal, predictive and precise.

The future of enterprise AI doesn’t start with the algorithm. It starts with the data lake that feeds it.

The post What Is an AI Data Lake? Benefits, Use Cases, and Best Practices appeared first on ITDigest.

Automated analytics is emerging as a core capability in next-gen IT performance monitoring. By embedding AI-driven analysis into infrastructure and observability tools, organisations are moving from reactive troubleshooting to proactive optimisation. This reduces mean time to resolve (MTTR) and protects user experience, service uptime and operational efficiency.

The Limitations of Traditional Monitoring Approaches

Legacy monitoring frameworks were built around static thresholds and pre-defined rule sets. While good in controlled environments, they struggle with the variability and scale of today’s systems. Thresholds need to be manually configured and tuned for each environment, which makes them prone to false positives or missed anomalies.

In addition, these systems only alert after performance metrics breach the set limits. By the time the alerts are triggered, user experience is already impacted. Root cause analysis often requires manual correlation across multiple logs, metrics and traces, which adds to the delay and operational overhead.

As environments scale and become more ephemeral, this reactive model is no longer sustainable. What’s needed is a shift from threshold based alerts to pattern based anomaly detection driven by analytics and machine learning.

In June 24, 2025, New Relic unveiled its AI Monitoring (AIM) offering, designed specifically for generative AI workloads. AIM introduces native observability for Model Context Protocol (MCP) servers, enabling full-stack insights into AI inference and agentic behavior. By correlating logs, metrics, and traces from AI models to application performance, it delivers plain-language explanations and automated actions integrated into platforms like GitHub Copilot and ServiceNow.

Automated Analytics as a Proactive Layer

Automated analytics puts intelligence in the middle of the IT monitoring stack. These systems learn normal behaviour patterns over time, across application workloads, network flows, user sessions and infrastructure performance.

When behavior strays from expected baselines, the system flags anomalies in real time. It does this before performance drops below critical levels. You can find small problems like memory leaks, slow networks, or latency spikes before they impact users. More importantly, it cuts down on static rules. This enables a quick response to changes in the system, deployment cycles, or user demand.

By putting analytics into observability platforms, you can create a self-correcting feedback loop. Performance insights are fed into config tools, auto scaling policies or incident response workflows, reducing downtime and operational friction.

Also Read: What Data Analytics Automation Means for Your Business Growth

Visibility Across the Full Stack

Next gen monitoring requires visibility across infrastructure, applications and digital experience layers. Automated analytics is great at high volume telemetry like system logs, API traces, real-time metrics and event data.

A slow response time for a microservice might connect to a memory spike on its server or a change in how the database queries behave. Automated systems show this insight in real time. This speeds up finding the root cause and fixing issues.

Cloud-native platforms gain the most from this method. This is especially true for those using containers and serverless components. Their short lifespan and complex connections make it hard for humans to see new patterns. AI-enabled analytics can find problems that manual dashboard checks might miss.

In May 2025, Virtana launched AI Factory Observability (AIFO), a next gen platform for modern, distributed IT environments. AIFO integrates telemetry across GPUs, CPUs, memory, storage and networking to provide full stack performance visibility.

The platform applies AI driven predictive analytics, root cause correlation and anomaly detection to proactively identify and resolve performance issues. It also includes a natural language interface to investigate anomalies with conversational queries.

Reducing Alert Fatigue and Improving Signal Quality

One of the biggest challenges in monitoring is alert fatigue. Operations teams get hundreds of alerts a day, many of which are redundant, low priority or irrelevant. This leads to slower response times and missed critical issues.

Automated analytics filters out noise by evaluating the context and severity of anomalies. Instead of flooding teams with every deviation, the system groups related events, ranks them by impact and provides a narrative of what’s happening.

This reduces cognitive load and lets teams focus on the most important issues. Over time systems learn from analyst feedback and improve signal quality, creating a virtuous cycle of operational intelligence.

Operationalizing Automated Analytics Across the Enterprise

Automating analytics requires more than technology, it means rethinking monitoring as a continuous intelligence function. Leading companies are embedding analytics into their observability platforms to consolidate telemetry from infrastructure, application and end-user layers. These platforms are the foundation for real-time insight generation and cross-system correlation.

Enterprises are also integrating analytics directly into CI/CD pipelines and DevOps workflows. During deployment, anomaly detection algorithms can validate system behavior, flag configuration drift or anticipate regressions. This feedback enables teams to address potential issues before they hit production or impact customers.

Beyond engineering, analytics insights are being surfaced to business operations teams. Performance deviations in a key transaction flow or spike in latency on a revenue-critical API are now reported not just as technical anomalies but as business risks. This alignment enables faster prioritization and more informed escalation paths across departments.

Innovation Without Compromise

In fast moving IT environments, speed and stability are at odds. Development teams must deliver new features. Meanwhile, infrastructure teams must ensure services stay up. Automated analytics helps meet these goals. It gives early warnings about risks. This way, teams can respond quickly and avoid downtime.

When system behaviour is continuously monitored in real time, organizations can innovate faster. New services, config changes or platform migrations can be monitored intelligently, with analytics detecting unintended consequences before they cascade into bigger disruptions.

This reduces rollbacks, minimizes firefighting and shortens the feedback loop between deploy and optimize. The result is an IT environment that’s not only more reliable but more responsive to business demands.

From Monitoring to Intelligence

Monitoring used to be about identifying known issues with static rules. Automated analytics is about system intelligence where platforms detect unknown patterns, learn from historical context and adapt to changing environments.

This changes IT monitoring from a reactive function to a strategic capability. It lets technology leader’s move beyond hindsight and into foresight. When done right automated analytics becomes a source of resilience, user happiness and competitive advantage.

Organizations use these insights for capacity planning, SLA enforcement, and service-level budgeting. IT helps drive enterprise growth by linking infrastructure behaviour to user experience and business performance.

From Observability to Business Value Alignment

Automated analytics is expanding beyond system health. It now helps connect technology to business outcomes. Platforms can track how slow systems affect revenue, customer churn, and SLA compliance.

CISOs, CIOs, and product owners are using these insights to make important decisions:

• Business-critical metrics: Transaction latency and checkout failures link directly to revenue.
• Experience analytics: Front-end performance connects to back-end systems, showing how changes impact customer behaviour.
• Cost-to-performance ratios: Calculated in real-time, allowing optimization of cloud spending without harming service.
• Board-level reporting: Automated narratives translate system issues into business risks or opportunities.

This shifts IT monitoring from a technical role to a business role.

Conclusion

As digital infrastructure becomes more complex and users expect more, IT teams can’t just react. Automated analytics helps team’s spot issues early and act fast. This keeps technology performance in line with business goals.

Smart analytics in performance monitoring tools helps us see everything, not just the problems. It eases operations, accelerates innovation, and enhances resilience in a cloud-first, data-driven world.

The post Automated Analytics and the Future of IT Performance Monitoring appeared first on ITDigest.

What is Sovereign AI?

Sovereign AI means building and running AI within a country, region, or organization’s own infrastructure. AI systems, models, and sensitive data used to train them are kept and managed within a trusted local environment. Sovereign AI is about having full command over where data lives, who can access it, and controlling AI outcomes. It is used by sectors with strict regulatory needs, such as healthcare, finance, defense, and government bodies.

Key features of Sovereign AI include:

Data Residency: Keeping data within specified borders.

Local Training: Developing AI models on site using local datasets.

Regulatory Compliance: Aligning operations with regional laws & standards.

Operational Control: Managing AI systems with local teams.

Why Train AI Locally?

Several factors encourage organizations to train and deploy AI in local or regional environments. Data needs to stay in designated areas and be processed according to local laws, per regulatory requirements like GDPR and AI Act. By keeping data out of prying eyes and foreign monitoring, local AI management improves security and privacy. This creates transparency and trust so companies can keep transparent audit trails and highest standards of AI systems. Local AI infrastructure provides flexibility and customization so companies can use data that can’t be shared or stored outside of their organisation to customise models. And building internal AI skills reduces reliance on international cloud providers who could be subject to foreign policy changes or availability issues.

How Organizations Are Building Sovereign AI

Organizations are creating sovereign AI through several approaches:

On-premise AI

• Installing AI platforms & servers in company-owned data centers or facilities.
• Only authorized personnel will manage hardware, software, and data.
• Used in banking, healthcare, and defense due to strict requirements for privacy and security.

Regional AI Hubs

• Governments and industry alliances set up regional data centers supporting collective AI projects.
• These hubs help smaller enterprises access compliant AI without massive investments in infrastructure.
• Often focus on job creation, innovation, and alignment with local energy and sustainability policies.

Hybrid and Multi-cloud Models

• Combining local (on-premise or regional) AI for critical workloads with public cloud resources for less sensitive tasks.
• Workloads are dynamically shifted based on operational, security, and compliance needs.

Also Read: Edge AI: Transforming Real-Time Data Processing Across Enterprise IT Ecosystems 

Investing in Sovereign AI Infrastructure

Investment in sovereign AI infrastructure is growing fast as companies realize they need control over data and AI systems. Market trends indicate that between 2022 and 2026, enterprise spending on AI systems would increase by 27% annually. In 2024, gen AI investment was over US$ 20 billion, including on-premises and cloud infrastructure. By 2028, 75% of enterprise AI workloads will be in hybrid environments for specific business needs, using both local and cloud resources.

A big chunk of this investment is going to sovereign AI. Governments and private sectors are prioritizing data sovereignty and compliance. For instance, Europe is investing billions of euros in national AI data centers. Countries in Asia Pacific are launching sovereign AI initiatives to build AI locally. Analysts estimate that sovereign AI infrastructure could be up to 30% of total AI infrastructure budgets by 2027, attributed to increasing regulation and geopolitical concerns.

The main reasons behind these investments are innovations and digital services, operational efficiency through intelligent automation, and market & regulatory demands for data sovereignty. Companies will be expected to justify these spend by showing clear ROI metrics like value creation, risk reduction, compliance and alignment with business goals. This growing investment is how sovereign AI infrastructure helps companies and governments stay competitive while meeting strict compliance requirements.

Compliance Challenges With Sovereign AI

Compliance is a big challenge for any company dealing with AI across borders. Every region has different regulations that create a complex landscape.

• The EU AI Act classifies AI applications by risk level and requires rigorous impact assessments for high-risk models.
• S. state laws often focus on transparency, thorough documentation, and granting consumers rights to appeal AI-driven decisions.
• Brazil and other countries emphasize transparency in AI use and restrict certain applications outright.

These diverse and conflicting rules increase operational complexity and raises compliance costs. To effectively address these challenges, organizations should follow several best practices.

Risk Assessment: Document risks of discrimination, data integrity, local law adherence before deployment.

Documentation & Reporting: Keep records of model purpose, data sources, known risks, mitigation strategies.

Human Oversight: Allow for human review and intervention for decisions that impact rights or have local impact.

Transparency: Inform users when AI is making decisions for them and provide explanations.

Privacy and Data Handling: Minimize, localize and retain data as required; use high security for sensitive information.

Global companies have to juggle many conflicting requirements and regulations in progress. However, this aids in maintaining compliance and trust across regions.

Case Studies

Government Initiatives

• European Union: Its digital sovereignty policies encourage states to set up national and regional AI clouds, keeping sensitive data within EU borders.
• France and Germany: They have invested in sovereign AI platforms and public AI hubs to support hospitals, financial institutions, and public administration.

Healthcare Providers

Many hospitals now train diagnostic AI models on their servers to comply with regulations like HIPAA and GDPR. This allows them to protect patient privacy and control sensitive medical records.

Banking and Finance

Banks deploy fraud detection and risk management AI systems within their networks. This ensures security and compliance with financial secrecy regulations.

Regional Consortia

In Asia and the Middle East, industry groups have launched regional AI centers that pool resources, allowing members to train and deploy AI while ensuring data sovereignty.

Future of Sovereign AI

Regulations are becoming stricter and cyber threats are getting bigger, giving the momentum for sovereign AI. Watch out for this:

1. Many companies will have a mix of on-premise and cloud infrastructure.
2. Industry groups and regulators will work toward common rules on AI explainability and compliance monitoring.
3. Countries will invest in talent, hardware, and software to encourage local innovation and reduce dependency.
4. Expect more transparency and user empowerment in AI-driven decisions.

In Conclusion

Sovereign AI is changing how businesses, governments, and institutions approach technology, compliance, and trust. By investing in local and regional AI environments, companies can secure sensitive data, comply with changing laws, and control digital assets. As the rules of the game change, those who act early to develop sovereign AI capabilities will innovate with confidence and meet society’s highest standards.

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This article looks at the business risks of medical device cybersecurity failures including legal exposure, reputational damage and competitive disadvantage. It also explores how proactive cybersecurity governance is becoming a differentiator in market access and enterprise valuation.

The Expanding Attack Surface for Connected Devices

The integration of Internet of Medical Things (IoMT) devices across healthcare environments has expanded the digital attack surface. Devices such as infusion pumps, patient monitors, diagnostic imaging equipment and wearable sensors are being connected to hospital networks, cloud platforms and mobile applications.

Each connection point is a possible point of entry for attackers. Attackers can use old firmware, poor authentication protocols or lack of data encryption to breach devices. In the worst-case scenario a breach can disable device operation, interfere with patient care or provide access to patient health information (PHI).

As threats increase in sophistication the monetary and reputational expense of a successful attack increases. Medical device cyber-attacks are not science fiction, they have been reported, dissected and in a few instances have resulted in recalls and regulatory attention.

Financial Exposure from Downtime and Legal Liability

Medical device cybersecurity failures can result in revenue loss across multiple dimensions. If a connected device is compromised its availability may be suspended until vulnerabilities are patched. In high dependency clinical settings this means workflow disruption, delayed treatments and loss of billable procedures.

In parallel healthcare providers and manufacturers face legal exposure. A device breach causing patient injury or unauthorized release of data can result in litigation, fines and compliance penalties under HIPAA, GDPR or state health data protection legislation. These expenses are compounded by internal investigations, remediation initiatives and reputational harm.

For device manufacturers vulnerabilities discovered post market can trigger costly product recalls, regulatory audits and market access restrictions. In some cases, cybersecurity deficiencies can delay FDA approvals or disrupt clinical adoption affecting revenue forecasts and shareholder confidence.

June 2025 – RunSafe Security, a U.S.-based cybersecurity firm released its 2025 Medical Device Cybersecurity Index. The report found 22% of healthcare organizations had incidents involving connected medical devices, 75% of them increased their OT security budgets and only 17% are confident in their current defenses. 83% of organizations now include cybersecurity in procurement processes and 80% are willing to pay a premium for devices with built in security. This is a clear sign that cybersecurity is no longer an afterthought but a key factor in purchasing decisions and patient safety in modern healthcare.

Incident Response Delays Multiply Business Impact

When device security incidents happen, the speed and clarity of the response has a direct impact on business recovery. Yet many manufacturers don’t have a structured response framework or real-time visibility into deployed devices. Without instant knowledge of which models, customers or software versions are impacted, organisations face delayed disclosure, extended downtime and increased wider financial exposure. Delayed response in some instances can result in legal non-compliance with obligations, regulatory fines, and only serves to further damage customer confidence and increase the cost of recovery compared with the breach.

Procurement Teams Now Consider Cybersecurity a Core Evaluation Criteria

Hospital procurement is changing. Clinical performance and cost are no longer the only decision metrics. Security posture is becoming a key filter and many health systems are requiring documented cybersecurity readiness before purchase. This includes vulnerability management policies, incident reporting workflows and integration with existing security infrastructure. Manufacturers who can’t meet these requirements may lose access to institutional buyers or face longer sales cycles due to repeated risk reviews. This shifts cybersecurity from a back office function to a go to market requirement and impacts sales velocity and long term competitiveness.

Legacy Devices are a Persistent Source of Exposure

Many organisations are still using legacy medical devices that lack modern security controls. These devices are still clinically functional but run on unsupported operating systems, have hardcoded credentials or no patching mechanism at all. For providers, maintaining these assets means long term exposure to preventable risk. For producers, open vulnerabilities in older products can present lingering liability even if the devices are out of production. Visionary organisations are therefore initiating phased remediation plans, ranging from network segmentation to controlled decommissioning, in an effort to mitigate their inherited risk profile and prevent unplanned downtime or incident response expenses.

Also Read: Cybersecurity in Connected Care: Securing IoT in the Healthcare Ecosystem 

Brand and Market Access Risks

In an industry built on trust and clinical precision, cybersecurity breaches have disproportionate brand consequences. Healthcare buyers and institutional partners are now evaluating cybersecurity as part of procurement and long term vendor partnerships. A breach tied to a medical device can erode provider trust and create long term reputational damage that’s hard to recover from.

Brand value in the medical device sector is tied to safety, reliability and regulatory alignment. A company known for lax cybersecurity can lose competitive positioning in high growth segments especially in diagnostics, remote care and digital therapeutics. This reputational damage doesn’t just affect sales, it can impact talent acquisition, investor sentiment and strategic partnerships.

Regulatory Scrutiny and Evolving Compliance Expectations

Governments and regulatory agencies are taking a more aggressive stance on medical device cybersecurity. In key markets like the US, the FDA now requires manufacturers to submit cybersecurity documentation as part of the premarket submission process. The FDA’s recent guidance outlines expectations for threat modeling, patch management, access controls and software bill of materials (SBOM) disclosures.

Noncompliance is no longer a minor deficiency. It can lead to approval delays or post market enforcement actions. The EU’s Medical Device Regulation (MDR) and Cyber Resilience Act impose cybersecurity responsibilities throughout the product lifecycle.

For global manufacturers, the cost of meeting these requirements is justified by the revenue risk of not meeting them. Cybersecurity maturity has become a prerequisite for regulatory success and long term market participation.

Cybersecurity as a Competitive Advantage

While the threat of cybersecurity risks is enormous in terms of downside exposure, a successful cybersecurity strategy can also generate value. Companies that bet on secure by design architectures, open vulnerability disclosure programs and real-time threat monitoring can become trusted partners to regulators, insurers and hospitals.

This trust translates into preferred vendor status, faster procurement cycles and stronger long term customer relationships. For digitally enabled care models like remote patient monitoring and virtual diagnostics, robust cybersecurity can become part of the value proposition.

Cybersecurity certifications and industry benchmarks are also influencing purchasing decisions. Companies that proactively demonstrate compliance with industry standards like ISO/IEC 27001, UL 2900 or NIST guidelines get faster access to hospital networks and payer ecosystems.

In May 2025, Medcrypt launched its Medical Device Product Security Intelligence Platform to help manufacturers proactively assess cybersecurity risks across the product lifecycle. The platform allows teams to quantify security threats in financial terms, prioritize vulnerabilities and generate remediation plans aligned to regulatory expectations. As cybersecurity becomes part of compliance and market access, tools like this are a sign of the shift from reactive security to value driven governance.

Security in Device Design from the Start

Medical device security can’t be an afterthought. To reduce lifecycle vulnerabilities, manufacturers are embedding security into the product development process. This means secure coding standards, formal threat modeling and penetration testing before a device hits the market.

Secure-by-design approaches encrypt data in transit and at rest, authentication for user access and safeguards to prevent tampering or unauthorized firmware updates. These technical controls are documented and tested to regulatory expectations, so you don’t have to do costly redesigns or post-launch patching.

By shifting security left in the development cycle, you reduce the need for reactive fixes and build market confidence in your product.

Post-Market Surveillance and Patch Management

A big part of medical device security is ongoing monitoring and vulnerability management. Even after launch, devices need continuous updates to address new threats that may emerge months or even years later.

Firms are spending money on patch delivery infrastructure in order to make secure and timely updates, particularly for equipment in the hospital setting where uptime is the priority. Remote update functionality with device telemetry allow detecting anomalies prior to them becoming incidents.

Concurrently, coordinated vulnerability disclosure programs enable third-party researchers to responsibly and safely report issues. Such programs are evidence of maturity and transparency and benefit your reputation among regulators and customers.

Cross-Functional Ownership and Risk Governance

Security isn’t just an IT problem. Effective programs span multiple domains, engineering, legal, compliance, product and executive leadership. Leading manufacturers have formalized cross-functional governance to oversee security strategy, incident response and regulatory alignment.

These governance models assign ownership for key tasks, from managing SBOMs to assessing third-party software risks. Boards and leadership teams are getting regular briefings on security risk exposure so they can tie technical vulnerabilities to financial impact.

In healthcare delivery organizations, biomedical engineers, IT security teams and clinical operations work together to ensure device usage doesn’t expose broader systems to compromise. This integrated governance framework recognizes security is not just a technical issue, it’s a business-wide priority.

Supply Chain Dependencies

Modern medical devices rely on complex supply chains with third-party software, hardware components and external development partners. These interdependencies expand the attack surface. Without visibility into the entire ecosystem, you may unknowingly ship devices with inherited vulnerabilities. To fix this, companies are doing more due diligence on suppliers and requiring secure development practices across the chain. Software bill of materials (SBOM) tracking, vendor risk scoring and supplier audits are being added to procurement workflows.

This supply chain visibility is important for security but also for compliance with regulations that now require documentation of third-party software components and their risks.

Cybersecurity as a Value Driver

The medical device industry is at a point where cybersecurity maturity is directly tied to revenue, brand and long term market access. As healthcare delivery models move towards connectivity, interoperability and data driven care any weakness in device security is a business liability.

But organizations that treat cybersecurity as a value generating capability, investing in design controls, transparent reporting and cross functional readiness are turning compliance into competitive advantage.

Rather than seeing it as a cost center, forward thinking companies are integrating cybersecurity into product roadmaps, go to market strategies and stakeholder communications. By doing so they not only reduce breach risk but also customer loyalty and adoption.

Conclusion

In today’s connected world of healthcare, medical device cybersecurity is no longer just a technical issue. It impacts clinical continuity, brand reputation, market access and long term financial performance. Medical device vulnerabilities can disrupt patient care, erode stakeholder trust and block commercial growth. By reframing cybersecurity as a business risk, medical device manufacturers and healthcare providers can approach it strategically. This means integrating security into product design, aligning to regulatory frameworks, supporting customer assurance and operational resilience. Those that do this across their ecosystem will be better placed to protect revenue, patients and lead in a digital healthcare world.

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