In Cloud Infrastructure news this week…

AWS Enhances Site-to-Site VPN with Flexible Bandwidth Modification Capabilities
Amazon Web Services has introduced enhanced bandwidth modification capabilities for its Site-to-Site VPN service. The update enables enterprises to dynamically adjust connectivity performance based on operational needs, improving flexibility and scalability. As hybrid and distributed work environments expand, adaptable cloud networking solutions are becoming increasingly important.

In Business Technology news this week…

CLA Launches CLA One Digital to Drive Business-Focused Digital Transformation
CLA has launched CLA One Digital, a platform designed to support business-centric digital transformation initiatives. The offering combines advisory expertise with technology services to help organizations modernize operations and improve decision-making. This reflects the growing convergence of consulting and digital innovation strategies.

In Cloud Security & AI news this week…

OpenAI Introduces Advanced Account Security Features to Advance User Protection
OpenAI has rolled out advanced account security features aimed at strengthening user protection across its AI platforms. The enhancements focus on authentication, monitoring, and threat prevention to safeguard enterprise and consumer interactions. As AI systems become more deeply integrated into workflows, robust security measures are critical for trust and adoption.

In Artificial Intelligence news this week…

AWS Introduces Generative AI Model Agility Solution
Amazon Web Services has launched a generative AI model agility solution to help enterprises manage and switch between AI models more efficiently. The platform is designed to reduce vendor lock-in and enable organizations to optimize performance across diverse AI workloads. This highlights the increasing need for flexibility in enterprise AI strategies.

In Quantum Computing news this week…

IBM Marks a Decade of Quantum Computing on the Cloud, Driving Global Innovation
IBM is celebrating ten years of cloud-based quantum computing innovation. Over the past decade, the company has expanded access to quantum systems for researchers, developers, and enterprises worldwide. This milestone reflects the growing maturity of quantum computing and its long-term potential across industries.

In FinTech news this week…

Anthropic Expands into Finance with AI Agents Built for Banking and Enterprise Operations
Anthropic is expanding into financial services with AI agents designed for banking and enterprise operations. These AI systems aim to automate workflows, enhance customer interactions, and improve operational efficiency in regulated environments. The move signals the rapid adoption of AI agents across the financial sector.

In HealthTech & Analytics news this week…

Ibex Medical Analytics Establishes Biopharma Advisory Board to Advance AI-Powered Precision Diagnostics
Ibex Medical Analytics has formed a biopharma advisory board to support the advancement of AI-powered precision diagnostics. The initiative aims to strengthen collaboration between healthcare and pharmaceutical stakeholders to improve diagnostic accuracy and accelerate personalized medicine efforts.

In IT Operations & Automation news this week…

Kyndryl Launches Agentic AI Capability to Prevent IT Outages and Accelerate Recovery
Kyndryl has introduced a new agentic AI capability designed to proactively prevent IT outages and speed up recovery processes. By leveraging autonomous AI agents, the platform can identify risks, automate remediation, and improve operational resilience. This reflects the growing trend toward self-healing IT environments.

Article of the Week

How Enterprises Are Using AI Agents to Run End-to-End Business Processes

AI agents are rapidly evolving from task-specific assistants into systems capable of managing end-to-end business processes. Enterprises are using these intelligent agents to automate workflows, coordinate operations, and improve decision-making across departments. As AI agents become more autonomous and context-aware, they are reshaping enterprise operations by reducing manual effort, increasing efficiency, and enabling scalable digital transformation.

The post ITDigest’s Weekly News Roundup Featuring AWS, CLA, OpenAI, IBM, Anthropic, Ibex and Kyndryl appeared first on ITDigest.

This innovative AI capability has been integrated within the Kyndryl Bridge platform, an AI-enabled open integration platform used by over 1,400 enterprise customers around the globe. According to Kyndryl, the platform currently generates over 16 million AI insights every month and has demonstrated reductions in IT incidents by up to 50%, while contributing to approximately $3 billion in annual customer savings through avoided outages and reduced maintenance costs.

The feature deploys AI agents to examine millions of observability signals across applications, infrastructure systems, and operational environments in real time. These AI agents are trained to spot slight anomalies and correlations that could be indicative of the system failing in the future, So giving IT teams a chance to act before the outage happens. Kyndryl mentioned that the feature is already in use on over 200,000 customer sites and is capable of handling more than 10 million incidents per year.

The new platform capability centers on AI-assisted root cause analysis. Usually, identifying the cause of a large enterprise outage can take days, or even weeks, of manual work across diverse hybrid cloud and multi-vendor environments. Kyndryl claims that its AI-based solution can greatly cut down the time needed for root-cause analysis, So enabling organizations to produce incident reports within hours rather than weeks.

Also Read: Anthropic Expands Into Finance With AI Agents Built for Banking and Enterprise Operations

The news is a sign of the wider change happening in the IT sector, with businesses being quick to incorporate agentic AI systems for automating infrastructure monitoring, incident management, cybersecurity operations, and service orchestration. Whereas traditional automation systems operate based on predetermined workflows, agentic AI platforms are capable of analyzing data independently, making contextual decisions, and carrying out corrective actions with little human assistance.

Implications for the IT Industry

Kyndryl’s launch highlights how AI is becoming deeply integrated into core IT operations and enterprise infrastructure management. The IT sector has traditionally operated under reactive paradigms wherein the team responds to an incident after it has occurred. However, agentic AI will allow the continuous analysis of IT operational data to detect patterns indicative of upcoming failures.

Such a change would be revolutionary in how IT Operations Centers, Managed Services, and enterprise IT support operates. Autonomous problem detection and resolution through AI-powered platforms will lower the reliance on human troubleshooting and increase the resilience of infrastructure in increasingly complicated hybrid cloud scenarios.

The emergence of observability platforms and the use of AI in IT operations, often referred to as AIOps, has already begun changing the game plans of organizations. The new capability of Kyndryl is another step towards that direction, as AI agents will not only detect potential issues but also take part in diagnosing and decision-making.

The innovation is also a clear indication of the rising competition between IT service providers and infrastructure companies to develop native AI platforms for operations. With the modernization of IT infrastructure in the enterprise space, organizations require intelligent systems for managing multi-cloud deployments, distributed computing environments, edge computing devices, and enterprise workloads.

Business Impact and Strategic Value

Business-wise, the financial consequences of IT outages can be quite large. This is truer for sectors like banking healthcare manufacturing, telecommunications, and retail, where downtime directly impacts sales, customer experience, and a company’s operational continuity.

Artificial intelligence (AI) powered predictive maintenance and outage prevention systems can be significant smart tools for businesses. They can help keep downtime low, enhance service quality, and also reduce costs. Besides, faster root-cause identification may also enable IT departments to fix problems more effectively so disruptions are kept to a minimum.

And, AI-powered agentic platforms might transform IT teams’ productivity by both relieving them of monotonous operational tasksand giving them more time to focus on such high-value activities as digital transformation, cybersecurity, or innovation planning.

Also, investment strategies may be affected by the drop in human interaction due to the use of AI-assisted IT operations. As a result, enterprises may dedicate more resources to infrastructure platforms exhibiting capabilities like observability, automation, and predictive analytics within a single operational ecosystem.

Yet, the rapid evolution of agentic AIs in enterprise IT is raising quite a few concerns about governance. The need for transparency, security, and human intervention will be A lot heightened as AI agents will be given more autonomy for running mission-critical systems.

The Future of AI-Driven IT Operations

Kyndryl’s new capability underscores a larger industry trend toward autonomous and predictive enterprise operations powered by AI. As enterprise environments become more distributed and data-intensive, traditional reactive support models may no longer be sufficient to maintain operational resilience at scale.

The growing adoption of agentic AI platforms suggests that future IT environments will increasingly rely on intelligent systems capable of identifying, diagnosing, and resolving issues before businesses are impacted. Companies that successfully integrate predictive AI operations into their infrastructure strategies may gain a significant competitive advantage through improved uptime, operational efficiency, and business continuity.

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For instance, these rapidly advancing AI features will allow AI agents not only to interact with customers and to perform routine tasks but also to access and manipulate business-critical content in a completely automated way. This new kind of AI can offer tremendous leverage in productivity but will also generate types of cyber risks that conventional security models have no answers for.

Cognizant said its new service focuses on establishing “provable trust” in AI systems through continuous monitoring and evidence-based assurance. Rather than relying on traditional static defenses, the platform is built to secure AI systems during both development and real-world deployment. The company noted that modern AI systems are probabilistic and context-aware, making them vulnerable to threats such as manipulated prompts, poisoned models, and compromised agent behavior.

Also Read: Transflo Unveils ‘Workflow AI for Factors’ to Modernize and Accelerate the Factoring 

“AI is fundamentally changing how enterprise systems behave,” said Vishal Salvi, Global Head of Cognizant’s Cybersecurity Service Line. “These systems are adaptive, context-driven and increasingly autonomous – and securing them requires continuous assurance across build and run-time environments. With Cognizant Secure AI Services, we are helping enterprises engineer trust into AI systems from day one and to sustain that trust as those systems evolve.”

The offering combines three major components: a secure Agent Development Lifecycle (ADLC) framework, Cognizant Neuro® Cybersecurity for unified threat monitoring and audit support, and Cognizant Trust, which provides policy enforcement, traceability, and responsible AI governance.

All these features together aim to cover the entire operational lifecycle for AI model security, data protection, identity management, AI DevOps security, and generative AI risk mitigation.

Based on Cognizant, they are currently helping over 250 companies in regulated industries to carry out AI security and digital transformation projects. Some of the first implementation examples include protection against deepfake fraud, model tampering, and risks related to autonomous AI agents operating within enterprise systems.

Research analysts working in the industry point out that businesses are looking more and more for integrated AI security systems that not only cover development but also operational environments. With the rapid spread of AI, there is a rising need for governance models that are capable of securing complex AI ecosystems while at the same time allowing for compliance and transparency.

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Security teams are under growing pressure to explain what is happening, why it matters, and what actions to take next. Yet manual threat research, retroactive hunting, and disconnected workflows continue to slow response and weaken confidence. With Threat Research Agent and ThreatWatch, Securonix helps organizations answer the questions that matter most during a major threat event: does this matter to us, were we exposed, and what should we do next?

The Securonix Threat Research Agent helps teams turn raw intelligence into structured, role-specific findings with source attribution and supporting evidence. By combining these capabilities with source attribution and supporting evidence, Securonix is helping teams move from data overload to decision-ready intelligence in minutes instead of hours. This capability is designed to improve communication across teams, strengthen executive confidence, and can reduce manual reporting effort by up to 70 percent.

ThreatWatch addresses the next operational gap: proving exposure. It monitors emerging threats curated by Securonix Threat Labs, automatically generates and executes SIEM queries, and runs retroactive sweeps across historical telemetry. Human validation is applied before escalation. Findings are surfaced through ThreatQ with direct pivots into the SIEM, giving teams documented, audit-ready answers when leaders need to know whether exposure was real.

Also Read: Zimperium Arms Security and Fraud Teams with AI-Empowered Mobile App Response Agent to Counter Growing Mobile Attacks

Together, Securonix and ThreatQ create a more connected intelligence-to-operations workflow. ThreatQ serves as the intelligence engine and experience layer where teams curate, investigate, and preserve context, while Securonix extends that workflow with AI-driven research, exposure validation, and operational evidence from the customer environment. The result is a more connected workflow that helps teams move faster from indicators to context, from alerts to proof, and from fragmented handoffs to more defensible action.

Securonix SynQ extends the workflow into the browser. Securonix SynQ lets analysts extract, validate, enrich, and curate intelligence directly from what they are reading, including blogs, reports, GitHub pages, and PDFs, then sync that work into ThreatQ investigations and workflows while surfacing relevant Securonix evidence and historical sightings. This reduces copy-and-paste research, preserves context, and helps analysts move faster from reading about a threat to operationalizing a response.

This announcement builds on recent recognition from QKS Group, which named Securonix (ThreatQ) a five time consecutive Leader in the SPARK Matrix: Digital Threat Intelligence Management, 2026. The recognition reflects ThreatQ’s role in helping organizations aggregate, enrich, and operationalize intelligence across the security ecosystem.

“Threat intelligence only creates value when it leads to action. What we are doing here is helping teams close the gap between knowing something matters and proving whether it matters in their own environment,” said Simon Hunt, Chief Product Officer of Securonix. “That means faster research, clearer validation, and better decisions when time and confidence both matter.”

By bringing together AI-powered threat research, continuous exposure validation, and human-backed confirmation, Securonix is extending ThreatQ with capabilities built for how modern security teams work. Analysts can reduce time spent searching and correlating intelligence. SOC leaders can improve consistency and escalation quality. Executives can gain clearer, risk-aligned reporting with evidence they can explain to auditors, regulators, and the board.

Source: BusinessWire

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Podboy has led large-scale security programs for federal agencies and global brands across retail, manufacturing, industrial, technology, media, and financial services. At Semperis, he will draw on his deep knowledge of the AI era and the escalating risks posed by an increasingly dynamic threat landscape.

“I am excited to join Semperis to help deepen both our internal and product security posture as we continue to scale and innovate for our customers,” said Podboy. “Semperis takes an identity-first approach to ransomware security, increasingly infused with AI-driven detection and response, helping organizations strengthen the resilience of their identity systems and crisis response processes to speed operational recovery in the event of an attack.”

Also Read: Deepwatch Appoints Brian Dhatt as Chief Executive Officer

Podboy brings a unique understanding of the business need for true resilience, heightened by his experience in leadership roles at organizations such as Fifth Third Bank, First Citizens Bank, Thomson Reuters and GE Digital, where he focused on building and scaling security programs that protect critical assets while supporting business growth.

A transformational cybersecurity leader, Podboy is known for turning complex strategy into clear, executable plans that empower teams and drive measurable business outcomes. He has scaled innovative security solutions across Fortune 100 organizations, accelerating growth, mitigating risk, and strengthening enterprise security postures while positioning security as a driver of business value and a seamless part of the user experience.

“John’s work scaling innovative security solutions across Fortune 100 organizations—accelerating growth, mitigating risk, and strengthening enterprise security postures—makes him a terrific addition to our leadership team,” said Sharon Vardi, Semperis Chief Operating Officer. “He has a strong record of building programs that put identity at the center of cybersecurity strategy. With John on board, we are further strengthening our commitment to help customers protect their hybrid identity environments and recover quickly when attacks happen.”

Source: PRNewswire

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An AI agent in an enterprise context is simple to understand if you strip away the noise. It is a system that can reason through a task, access tools or systems, and act with a level of autonomy while still staying within defined boundaries. It does not just suggest. It executes.

That said, enterprises are not handing over full control. Reliability is still evolving, and that is why most real deployments include human-in-the-loop checkpoints. The agent works, but a human validates critical steps. This balance is what makes the model usable in production.

At the same time, AI itself is no longer experimental. According to Microsoft, global adoption has reached a point where roughly one in six people were already using AI tools by late 2025.

That scale changes expectations. Enterprises are not asking if they should adopt AI. They are asking how far they can push it.

The Agentic Surge Why This Is Happening Now

Enterprises have spent the last decade building digital infrastructure. CRMs, ERPs, support platforms, analytics tools. On paper, everything is connected. In reality, most workflows still rely on people moving information between systems.

This is the gap AI agents are stepping into.

Instead of adding another tool, agents sit across tools. They pull data from one system, process it, and trigger actions in another. They act as connective tissue between fragmented stacks.

This is not a fringe trend. According to McKinsey & Company, 62 percent of organizations are already experimenting with AI agents.

At the same time, the business case is getting clearer. PwC reports that 66 percent of companies are already seeing measurable productivity gains from these systems.

So the shift is not theoretical. It is operational. Enterprises are moving from isolated automation to coordinated execution.

Operations and Supply Chain Moving Toward Autonomous Procurement

This is where AI Agents in Business Processes start to show real weight. Operations is messy, data-heavy, and full of repetitive decision loops. That makes it ideal for agent-driven execution.

Take procurement.

Traditionally, inventory teams monitor stock levels, raise requests, compare vendors, negotiate pricing, and generate purchase orders. Each step sits in a different system.

Now imagine this flow with an agent.

An agent connected to SAP detects a drop in inventory. It checks historical demand patterns. Then it pulls vendor options from Oracle systems. It evaluates pricing trends, flags preferred suppliers, and drafts a purchase order. Before final submission, it routes the request through ServiceNow for approval.

No single step is new. What is new is that the sequence runs without manual stitching.

However, it is important to stay grounded. According to McKinsey & Company, most deployments today are still limited to one or two functions rather than full end-to-end orchestration.

This matters. It keeps expectations realistic. Enterprises are not running fully autonomous supply chains yet. They are building toward it, one workflow at a time.

Customer Support Moving From Triage to Resolution

Customer support is often the first place where companies experiment with AI. But the shift now is not about answering questions faster. It is about solving problems completely.

Traditional automation stops at triage. It categorizes tickets, suggests replies, and routes issues.

AI agents go further.

Consider a refund request.

An agent pulls customer data from Salesforce. It checks order status in a logistics platform. Then it verifies payment details and processes the refund through a payment gateway. Finally, it updates the ticket and notifies the customer.

The entire flow happens within one coordinated loop.

This is where AI Agents in Business Processes become visible to the end user. Response time drops. Resolution quality improves. At the same time, support teams shift from handling tickets to supervising systems.

The impact is not just operational. It changes how support is perceived. It moves from reactive service to controlled execution.

Also Read: How to Choose the Right SaaS Platform for Your Business: A Strategic Guide for Enterprise Decision-Makers?

GTM Execution Redefining Sales and Marketing Workflows

The biggest untapped opportunity sits in go-to-market functions.

Sales and marketing teams spend a surprising amount of time on preparation. Researching accounts, building decks, updating CRMs, qualifying leads. These are high-effort, low-differentiation tasks.

AI agents compress that effort.

An agent can scan a prospect’s latest filings, extract key signals, and build a tailored outreach narrative. It can enrich contact data using platforms like HubSpot, draft communication, and log interactions automatically.

More importantly, it creates what teams call warm handoffs.

Instead of passing raw leads, marketing passes context-rich opportunities. Sales steps in with insight already in place.

This is where AI Agents in Business Processes shift from efficiency tools to revenue enablers.

The difference is subtle but important. It is not about doing the same work faster. It is about changing what work gets done by humans in the first place.

Governance and the Trust Layer Enterprises Cannot Ignore

This is where most conversations get uncomfortable.

Adoption is rising. Use cases are expanding. But scaling impact is still a challenge.

According to McKinsey & Company, only 39 percent of companies have achieved enterprise-level financial impact from AI.

That gap is not about model capability. It is about governance.

Enterprises face three core issues. First is control. Agents can access multiple systems, which increases risk if permissions are not tightly defined. Second is transparency. Decision paths are not always visible. Third is reliability. Outputs still require validation.

This is why leading organizations are building what can be called a trust layer.

Permissions define what an agent can access. Audit logs track every action. Spend limits prevent uncontrolled execution. And human checkpoints remain in place for critical decisions.

Institutions like the World Economic Forum and the European Commission are also shaping how responsible AI should be deployed at scale. At the same time, research bodies such as Stanford University and MIT continue to push frameworks that balance innovation with accountability.

So the real constraint is not whether agents can act. It is whether enterprises can trust them to act safely.

How Enterprises Can Start Building Agentic Workflows

The transition from AI-enabled to AI-led operations does not start with technology. It starts with clarity.

The first step is a process audit. Identify workflows that are high volume, repetitive, and spread across multiple systems. These are the best candidates for agent-driven execution.

Then define boundaries. What should the agent handle fully, and where should humans step in. This is where most failures happen. Not because of poor models, but because of unclear control structures.

Next, start small. One workflow. One department. Prove value. Then expand.

The goal is not to build a single all-powerful system. That idea is still unrealistic. The real future looks different.

It is a system of agents. Each one focused on a specific function. Each one connected. Each one operating within defined limits.

That is how AI Agents in Business Processes will scale. Not as a replacement for enterprise systems, but as the layer that finally makes them work together.

The post How Enterprises Are Using AI Agents to Run End-to-End Business Processes appeared first on ITDigest.

Mobile applications are now one of the most exposed and least protected attack surfaces in the enterprise, leading cybercriminals to adopt a mobile-first attack strategy. Mobile banking malware-driven transactions increased 67% year over year in 2025, according to the Zimperium 2026 Mobile Banking Heist Report. SOC and Fraud teams face skyrocketing volumes of highly sophisticated mobile attacks powered by weaponized AI coupled with social engineering campaigns.

With Mobile App Response Agent security and Fraud analysts are able to trigger an automated investigation with just a single action, immediately analyzing reams of mobile app alerts on a device and determining whether they resulted in a real incident or fraud, then delivering a clear attack narrative and recommended actions for response.

Also Read: Cyborg Partners with Austin Artificial Intelligence to Deliver End-to-End Secure AI in Production

“For the first time, every SOC and Fraud analyst can investigate mobile app threats with the depth of a mobile security specialist,” said Chris Cinnamo, Deputy CTO at Zimperium. “Investigation times drop from hours to minutes, making the agent a true force multiplier for these teams.”

Key Capabilities:

• Incident Discovery: Confirms incidents with confidence score
• Event Correlation: Brings related mobile signals into a single, contextual incident view.
• Attack Context: Delivers plain language threat narratives and risk analysis
• Remediation Guidance: Provides clear response actions

Source: PRNewswire

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CyborgDB is the end-to-end-encrypted vector database purpose-built for regulated industries, allowing organizations to build and scale high-performance AI systems without compromising security or compliance. Built for real-world AI, CyborgDB possesses sub-millisecond latency for searching hundreds of millions of vectors without exposing a single byte of plaintext.

Security organizations like OWASP have warned that vectors and embeddings represent a fast-emerging area of vulnerability, while Fortune Business Insights predicts that the vector database market will grow to nearly $18 billion by 2034. This partnership underscores a broader industry shift in focus from AI that works to AI that works securely in production.

Also Read: OPAQUE Acquires Abu Dhabi-Developed Cryptographic AI Technology

“AI adoption is accelerating, but security has lagged behind,” said Nico Dupont, Founder and CEO of Cyborg. “This partnership with Austin AI shows that secure, production-ready AI infrastructure is not just possible – it’s already being deployed.”

Austin Artificial Intelligence is widely recognized as a leading consultancy, partnering with organizations to embed AI across strategy, operations, and decision-making. This partnership reduces the friction caused between AI moving into production and security being a productivity inhibitor, embracing secure AI as the industry standard.

“Our customers want to deploy AI on sensitive data without introducing new risk,” said Robert Corwin, CEO of Austin Artificial Intelligence. “Partnering with Cyborg allows us to confidently bring end-to-end security into production AI systems, giving enterprises the confidence to move faster and scale AI safely.”

SOURCE: Businesswire

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With AI adoption accelerating across departments such as HR, finance, engineering, and customer operations, enterprises are increasingly relying on AI assistants, coding agents, and MCP-connected tools to handle sensitive data and automate workflows. However, this rapid adoption has introduced new vulnerabilities, particularly from “shadow AI” tools unsanctioned or poorly governed applications that operate outside traditional security visibility.

Unlike legacy security solutions such as endpoint detection and response (EDR), CASBs, and network monitoring tools, which focus on processes and traffic, modern AI systems operate within encrypted environments and semantic layers that remain largely invisible to conventional defenses. This creates a significant security gap, especially as AI agents gain the ability to reason, act, and interact autonomously within enterprise systems.

“Security leaders are waking up to the reality that their AI exposure doesn’t live in one place it lives everywhere their employees and agents do,” said Vrajesh Bhavsar, CEO and Co-Founder of Operant AI. “The endpoint is where AI actually meets the workforce. It’s been the largest blind spot in the enterprise security stack and it’s the gap we built the Operant Endpoint Protector to close.”

Addressing the Rise of Shadow AI and Agentic Risks

The emergence of AI-powered coding environments and agent-based workflows has significantly improved productivity but also introduced new threat vectors. These include prompt injection attacks, data leakage, unauthorized access to sensitive systems, and real-time execution risks within AI-driven processes. MCP, which acts as the connective layer enabling AI agents to interact with tools and services, further expands the attack surface by allowing actions across trusted and encrypted channels.

Operant Endpoint Protector is designed to counter these risks by providing continuous visibility and control over AI interactions. The solution enables enterprises to monitor every AI prompt, MCP server, plugin, and agent activity in real time ensuring that even previously hidden or unmanaged AI tools are brought under governance.

Also Read: Malwarebytes Integrates Real-Time Scam Detection into Claude AI Conversations 

Core Capabilities for Enterprise-Grade AI Security

The platform delivers a comprehensive set of features tailored to the complexities of modern AI environments:

• Enterprise AI & MCP Registry: A centralized inventory that maps all sanctioned and shadow AI tools, MCP servers, and integrations, offering visibility into usage patterns and risk profiles.
• Real-Time Detection and Agent Monitoring: Continuous tracking aligned with emerging AI security frameworks to identify threats such as prompt injections, identity anomalies, and data exfiltration attempts within encrypted channels.
• Inline Data Protection: Enforcement of policies for sensitive data such as PII, PCI, and PHI, including automatic redaction of confidential information during AI interactions.
• Access and Execution Governance: Role-based access controls and runtime guardrails that ensure AI agents operate strictly within authorized boundaries.
• Endpoint-Native Code Protection: Built-in defenses against code injection and malicious execution attempts, with controls such as rate limiting and token management.

These capabilities are supported by a compliance-focused architecture that includes audit logging and data sovereignty controls, making the solution suitable for highly regulated industries such as financial services, healthcare, and government sectors.

Built for Scale, Privacy, and Enterprise Deployment

Operant Endpoint Protector is delivered as a native application compatible with macOS, Windows, and Linux, enabling seamless deployment across enterprise environments via standard device management workflows. It integrates with identity providers (IdPs) to align AI governance with existing access management frameworks, ensuring consistent enforcement of security policies across users, roles, and AI interactions.

Additionally, the platform supports private deployment models, including hybrid and private-SaaS configurations, allowing organizations to maintain full control over sensitive data and AI activity logs. This is particularly critical for enterprises operating under strict data residency and compliance requirements.

The post Operant AI Introduces Endpoint Protector to Address Enterprise AI Security Blind Spots appeared first on ITDigest.

The company also announced the promotion of Chief Product and Technology Officer Anand Ramanathan to President, underscoring Deepwatch’s commitment to scaling its platform, accelerating product innovation, and strengthening its leadership position in AI-driven security operations.

Dhatt brings more than two decades of experience building and scaling global, enterprise-grade technology platforms. Most recently, he served as Chief Technology Officer at BigCommerce and prior to that, Borderfree. Both companies achieved successful IPOs during his tenure. Across his career, Dhatt has led large, distributed engineering and product organizations and driven transformation across high-growth, mission-critical platforms serving global enterprises.

“Deepwatch has a unique and powerful foundation—more than a decade of real-world security operations, a rich proprietary dataset, and AI-driven automation already delivering results at scale,” said Brian Dhatt, CEO of Deepwatch. “What sets Deepwatch apart is how we combine that automation with human expertise. As we continue to evolve toward an Agentic AI SOC model, our focus is on using AI to handle scale and speed while keeping humans in the loop to ensure accuracy, trust, and real-world outcomes.”

Also Read: Point Wild Appoints Neill Feather Chief Executive Officer

Deepwatch has established a differentiated market position through an AI-driven, human-led SOC model. The Deepwatch Guardian MDR Platform combines:

• More than ten years of real-world SOC telemetry and operational data
• A proprietary dataset spanning detections, investigations, response workflows, and outcomes
• Agentic AI-driven automation and orchestration deployed in production
• Human-in-the-loop validation to ensure accuracy, context, and continuous improvement
• Deep integrations across enterprise security ecosystems

This foundation enables organizations to accelerate adoption of autonomous security operations while maintaining the visibility, control, and expert oversight required for enterprise environments. Rather than replacing human analysts, Deepwatch’s approach uses AI to reduce noise, automate investigations, and surface the highest priority threats—allowing experts to focus on decision-making and response where it matters most.

“Brian is the ideal leader for Deepwatch’s next chapter,” said Bill Phelps, Chairman of Deepwatch’s Board of Directors. “His experience scaling complex technology platforms and leading organizations through periods of rapid growth will be invaluable as the company continues to extend its leadership in autonomous security operations.”

Ramanathan brings more than 20 years of product and go-to-market leadership across leading cybersecurity and cloud companies, including McAfee, Proofpoint, Cisco, and Skyhigh Security. Over his career, he has built and scaled market-leading security platforms, and driven category-defining innovation across cloud, endpoint, and data security. As President, Ramanathan will expand his responsibilities to oversee product, operations and marketing, ensuring tight alignment between innovation, go-to-market execution, and customer outcomes.

“Anand is a proven leader with deep domain expertise and a strong track record of building category-leading security platforms,” said Dhatt. “His expanded role ensures we continue to translate our unique combination of AI-driven automation and human expertise into scalable, differentiated outcomes for our customers.”

Backed by Springcoast Partners, Goldman Sachs, ABS Capital and Splunk Ventures, Deepwatch represents a fundamental shift from traditional managed security models toward autonomous SOC operations. By combining AI-driven operations with human oversight, the company delivers continuous improvement, higher accuracy, and enterprise-grade trust—enabling organizations to adopt autonomous security with confidence.

Source: BusinessWire

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