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Nowadays, ransomware is so fast and stealthy that letting a security analyst manually handle an alert, examine the potential threat, and isolate the infected equipment can almost be the death of the company.

Recognizing that the threat containment window has shrunk to seconds, data infrastructure pioneer NetApp and networking and security giant Cisco announced a major expansion of their long-standing collaboration. The cornerstone of this release is the launch of the NetApp Splunk Security Orchestration, Automation, and Response (SOAR) playbook. By embedding programmatic incident response directly into the storage hardware layer where a company’s critical data actually resides, the alliance effectively moves corporate defense from a slow, reactive posture into a unified, machine-speed fortress.

Active Threat Containment at the Storage Layer

Logging and aggregating security telemetry is standard operational practice across enterprise systems. The challenge occurs when a live encryption sweep begins, and data protection software is unable to halt execution before massive corruption spreads.

The net-new capability introduced by Cisco and NetApp addresses this roadblock by integrating Cisco’s Splunk SOAR workflow engine directly with NetApp’s core data management platform, ONTAP. Splunk Enterprise Security already relies on NetApp Ransomware Resilience analytics to flag active network and data anomalies. This new, downloadable SOAR playbook takes those real-time data-layer alerts and immediately maps them to deterministic, automated containment playbooks.

When a suspicious activity signature is verified, the system executes defensive actions without waiting for manual human approvals:

Account Isolation: Instantly blocks compromised or malicious corporate user accounts from accessing network shares or corporate file hierarchies.

Immutable Snapshots: Triggers unalterable, cryptographic snapshots of the underlying data volumes, preserving clean historical restore points right at the moment of infection.

Volume Shut-Downs: Logically forces compromised data pools completely offline to physically compress the “blast radius” of a threat.

Also Read: The Agentic Shift: How Experian’s Agent Operating System is Redefining Financial Services

Transforming the IT and Infrastructure Industry

The introduction of direct storage-layer automation creates fundamental waves across the broader information technology (IT) and system engineering sectors.

Eliminating the Storage-Security Silo
Historically, corporate IT organizations have been divided into structural silos: storage administrators focused heavily on hardware availability, IOPS (Input/Output Operations Per Second), and capacity, while security operations center (SOC) analysts managed firewalls and network perimeters. This release signals a market shift where storage hardware can no longer remain a passive endpoint. As defense-in-depth strategies become a non-negotiable standard, competing infrastructure and cloud backup vendors will face heavy market pressure to co-develop similar active hardware integration tools, effectively changing the baseline architecture of enterprise storage.

Shifting SOC Metrics and Labor Economies
The global IT landscape continues to face extreme technical skill shortages and unprecedented security team burnout. By rolling out pre-built, out-of-the-box automated orchestration scripts, NetApp and Cisco target a foundational engineering metric: Mean Time to Contain (MTTC). Shrinking the latency between attack detection and containment from hours to milliseconds transforms the daily routine of IT professionals. Rather than writing fragile, custom bash scripts to pass commands between vendor tools, IT engineers can shift their career trajectories toward proactive risk architecture, identity management, and holistic digital resilience.

Broad Operational Impact on Enterprise Businesses

For companies balancing digital transformation with hyper-aggressive threat environments, the deployment of machine-speed data protection fundamentally alters operational dynamics.

Minimizing Downtime and Financial Losses
When ransomware slips past outer barriers, every single minute of encryption latency costs thousands of dollars in lost operational capacity, compromised client trust, and regulatory penalties. Because the NetApp Splunk SOAR playbook segments the threat instantly, companies restrict attacks to fractional pools of data. Instead of a catastrophic, business-wide network blackout that stretches across multiple weeks of manual recovery, an enterprise handles a highly managed, localized cleanup of an isolated volume, maintaining normal client-facing operations.

Achieving Predictable Business Continuity

Relying entirely on bulk nightly backups for disaster recovery is no longer sufficient; transferring petabytes of business data back across an enterprise network takes hours or days, and often, secondary backup servers are target objects for modern malware anyway. Utilizing real-time volume snapshots and automated network cleanrooms gives corporate boards predictable, measurable resilience numbers.

This transparent audit trail allows compliance teams to satisfy rigorous regulatory framework parameters, lower cyber insurance policy premiums, and ultimately safeguard corporate equity.

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Enterprise AI and HPC teams pay a compounding price for every cloud (or system) they run on: rebuilt pipelines, rewritten deployment scripts, profiling, testing and validation, before a single workload can run on a new infrastructure. That cost scales directly against the speed the business demands. Fuzzball eliminates it and completely levels the playing field.

A genomics team that validates a sequencing pipeline on AWS moves it to Azure or OCI without modifying a single line in the workflow definition. A model training job that requires H100 density routes to CoreWeave automatically, while a data-sensitive simulation stays on-premises by policy. The workflow definition, container images, data orchestration and job sequencing remain identical across every environment. Teams reach production faster, access better GPU capacity at lower cost and carry no operational overhead for every cloud they add.

Also Read: Beacon.li Launches Implementation Studio, the First AI Platform to Execute Enterprise Software Implementations End to End

“AI teams today are asked to ship faster, control costs and maintain sovereignty over their data, simultaneously, across infrastructure that was never designed to work together,” said Gregory Kurtzer, CEO and founder of CIQ. “We built Fuzzball to solve that problem at the architectural level. When your workflow definition abstracts its requirements properly, you get portable access to every GPU environment the market offers and the freedom to route to wherever the best price, performance and data policy lives. Controlling your infrastructure and workloads is what enterprise AI infrastructure requires for production, and no other platform delivers it.”

One control plane across five clouds and on-premises

Fuzzball’s multi-cloud architecture rests on a provider-agnostic workflow definition. The file that describes compute jobs, data movement, container images and resource requirements carries no cloud-specific logic. Fuzzball’s orchestration layer translates that definition into concrete infrastructure on whichever environment sits underneath, whether that means Google Cloud, Microsoft Azure, Oracle Cloud, AWS or CoreWeave.

Fuzzball federates across all five cloud environments, alongside on-premises clusters. It simultaneously evaluates available environments at runtime and routes each job to its optimal destination. Enterprises gain the GPU density of CoreWeave, the breadth of three major hyperscalers and the sovereignty of on-premises infrastructure from one control plane, with no separate toolchains, deployment scripts or IAM models per provider.

One security model across every environment

Each cloud deployment is provisioned through a two-phase automated process that stands up a complete, production-ready cluster without manual intervention. Fuzzball maintains one IAM model, one set of RBAC policies and one secrets management posture across every cloud it runs on. Static credentials are eliminated at every layer: Workload Identity on GCP, Managed Identities on Azure, Dynamic Groups on OCI and IAM Roles on AWS. Security and compliance posture travel with the workflow, not the cloud.

“Fuzzball turns multi-cloud from a liability into a competitive advantage,” said Bjorn Hovland, president of CIQ. “Five clouds used to mean five IAM models, five deployment pipelines and five sets of operational overhead, with complexity and risk being multiplied.”

Source: PRNewswire

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Also Read: Didit Raises $7.5M to Advance AI-Native Identity Infrastructure 

Adnan Amjad, US Cyber Leader at Deloitte, highlighted the transformative shift, stating, “We are at an inflection point where traditional approaches to security operations can no longer keep pace with the threat landscape. By bringing together Google Cloud’s agentic AI, Wiz’s deep cloud context and Deloitte’s security integration experience, we are helping to pioneer a new paradigm. This human-led, AI-powered approach allows organizations to build resilience by design and manage cyber risk at the speed of business.” Furthermore, Francis deSouza, Chief Operating Officer at Google Cloud and President of Security Products, emphasized the scale of the deployment, adding, “You can’t bring traditional security to an AI fight. AI Threat Defense combines Wiz’s multicloud visibility with Google’s AI and security expertise. Collaborating with Deloitte to deliver customizable, agentic defense helps us scale this offering, empowering organizations to stop AI-driven threats at machine speed.”

Read More: Deloitte Collaborates with Google Cloud and Wiz on Human-in-the-Loop, AI-Powered Cyber Defense

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Even though companies are using Artificial Intelligence they still encounter problems like data silos, outdated technologies, and the difficulty of integrating AI into daily activities. Cognizant is planning to solve these problems via its AI Builder approach, which merges state-of-the-art AI technologies, knowledge of different industries, and design centered on the user’s experience to help businesses see tangible results in a short period of time.

“When the data foundation is AI-ready and business context is built in, build cycles collapse,” said Naveen Sharma, Senior Vice President and Practice Head, AI & Analytics at Cognizant. “That is exactly what Cognizant’s AI Builder approach delivers, and with Snowflake CoCo, we are putting production-grade AI into enterprise workflows in hours, not weeks.”

Also Read: Incorta Launches Real-Time Data Foundation for Workday Adaptive Planning

The collaboration has already demonstrated significant momentum within Cognizant’s AI and Analytics practice. The CoCo platform has been adopted by more than 2,250 users across internal teams and client environments, supporting over 30 enterprise use cases. Cognizant has also developed numerous custom CoCo capabilities and AI accelerators that have collectively powered more than 1.3 million AI-driven requests.

A great example of this is A+E Global Media, where Cognizant put in a conversational analytics agent driven by Snowflake CoCo. This tech took care of reporting and data analysis jobs that used to be done manually. Now, business folks can query complex data sets using normal language. As a result, tons of time was saved, and it made ops more efficient and helped with better decision-making throughout important divisions.

Beyond just analytics, Cognizant is now pushing CoCo-powered solutions into other areas like contract smarts, compliance automation, finance ops, and anomaly detection. Companies see huge drops in manual work, major cost savings, and swifter process times thanks to these efforts.

Cognizant and Snowflake keep pouring money into special AI skills for different industries, plus prebuilt agent templates and orchestration abilities. Their goal? To give companies a way to easily deploy agentic AI and turn data into real business insights.

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As part of the program, TrendAI will use Anthropic’s Claude Mythos Preview to support the review and analysis of software code, helping threat intelligence researchers turn accelerated vulnerability discovery into coordinated disclosure, prioritized remediation, and measurable risk reduction through vulnerability shielding and virtual patching.

AI is dramatically accelerating vulnerability discovery. TrendAI views this as a positive signal for the industry – it is part of the broader, collaborative ecosystem TrendAI has been actively contributing to for decades alongside organizations like Anthropic.

Also Read: Tanium and Censys Join Forces to Accelerate Continuous Exposure Management in the Age of AI

Rachel Jin, Chief Platform and Business Officer, Head of TrendAI: “We’re aligned with Anthropic’s goals of using AI to make all software more secure. Organizations increasingly depend on software that operates at tremendous scale and supports critical business functions. Project Glasswing represents an important opportunity to explore how advanced AI can help software providers identify vulnerabilities earlier and improve the security and resilience of the systems customers depend on every day.”

TrendAI joins a growing community of organizations participating in Project Glasswing to better understand how frontier and advanced AI models can support defensive security efforts and improve the security of critical software infrastructure.

Insights gained through the program will contribute to informing the broader industry efforts to strengthen the security of the digital ecosystem.

Source: PRNewswire

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They have a decision-making problem.

The breach, the ransomware attack, the leaked credentials, the compliance failure. Those things usually show up much later. The real issue starts much earlier when security sits in one corner of the organization while the business keeps moving in another direction.

That approach worked when networks were smaller and employees sat inside the same office. It breaks down fast in a world filled with cloud platforms, remote work, connected vendors, AI tools, and constantly expanding digital footprints. The perimeter is gone. What remains is risk, and that risk needs structure.

A broad cybersecurity framework kind of gives organizations that structure, it helps in a practical way. It provides a system for steering security, spotting risk, choosing which controls matter most, handling incidents, and then getting better over time. What’s more, it makes security blend into business strategy, not just sit there as an IT checklist, you know the one that only gets real attention once something goes wrong.

Phase 1: Aligning Cybersecurity with Corporate Governance

Securing Executive Buy-In and Defining Ownership

One of the biggest mistakes organizations make is thinking that cybersecurity is really only for the security team. It’s not.

Because security choices touch legal exposure, customer trust, operational continuity, revenue, and even brand reputation. If you look at it through that lens, then cybersecurity turns into a leadership matter first, before it ever turns into some kind of tech problem.

The board should own oversight. Executive leadership should define priorities. The CISO should drive execution. Meanwhile, departments like HR, Legal, Compliance, Procurement, and Operations should understand exactly where they fit into the picture.

This shift is already happening. According to PwC’s 2026 Global Digital Trust Insights, 60% of business and technology executives rank cyber risk investment among their top three priorities. That number kind of matters, because it shows that cybersecurity has slid into the same room as growth, efficiency, and business resilience, like it’s no longer just a separate issue.

Another step that is often overlooked is setting up an Enterprise Risk Appetite Statement. It feels like corporate jargon until you catch what it actually does. It forces leadership teams to answer a simple question. How much cyber risk are we willing to tolerate before business objectives are affected?

Without that answer, every security decision becomes a debate.

Conducting a Comprehensive Asset Inventory and Risk Assessment

You cannot protect what you cannot see.

That line gets repeated often because it remains true.

Before organizations talk about controls, they need visibility. They need to know where critical data lives, who can access it, how sensitive it is, and which systems support business operations.

Start with classification. Separate public information from confidential information. Separate customer records from internal documents. Identify critical applications, cloud assets, endpoints, databases, and third-party integrations.

Only then does risk assessment become meaningful.

Many organizations are realizing this. Microsoft’s 2026 Data Security Index found that more than 80% of surveyed organizations are implementing or developing Data Security Posture Management strategies. That trend says something important. Security leaders are spending less time guessing where their data sits and more time building visibility before building controls.

Phase 2: Selecting and Adapting a Standardized Framework Architecture

Comparing NIST CSF 2.0, ISO/IEC 27001, and CIS Controls

A cybersecurity framework does not need to be invented from scratch.

In fact, trying to build one from scratch is usually a mistake.

Established frameworks already contain years of security lessons, operational experience, and industry best practices.

NIST CSF 2.0 works well for organizations that want flexibility and a risk-driven approach. ISO/IEC 27001 is often attractive for organizations operating across multiple jurisdictions because it provides a formal management framework. CIS Controls offer practical security actions and are often easier for operational teams to translate into day-to-day activities.

The better question isn’t which framework is best.

The better question is which framework aligns with your business, industry obligations, resources, and risk profile.

One interesting development is the addition of the Govern function within NIST CSF 2.0. That change reflects where cybersecurity is heading. Governance now sits at the front of the conversation instead of being treated as an afterthought.

Technology matters. Governance decides whether technology succeeds.

Phase 3: Architecting Controls and Defense-in-Depth Policies

Implementing Zero Trust Architecture and Access Controls

For years, organizations built defenses around the assumption that users inside the network could generally be trusted.

Attackers loved that assumption.

Zero Trust turns that idea upside down. Verification becomes continuous. Access becomes conditional. Trust isn’t just handed over; it has to be earned more than just assumed.

So, yeah, your kind of implement Multi-Factor Authentication, you follow the Principle of Least Privilege, you slice the access around identity and you keep the authorizations that aren’t needed, across the entire environment, kind of locked down.

And the point isn’t to add annoyance. The point is to shrink the chances for misuse and sideways movement when something bad happens, because inevitably something does go wrong.

Also Read: Security Challenges for Smart Medical Devices in Hospitals: How Healthcare Providers Can Reduce Cyber Risk 

Vulnerability Management and Continuous Threat Monitoring

Every system contains weaknesses. The question is whether defenders find them before attackers do.

This is where vulnerability management becomes one of the most important parts of a cybersecurity framework.

Organizations should continuously scan for vulnerabilities, prioritize remediation efforts, maintain disciplined patching schedules, and use SIEM and EDR technologies to monitor activity across the environment.

The urgency is hard to ignore. IBM’s X-Force Threat Intelligence Index 2026 reported a 44% year-over-year increase in exploitation of public-facing software or system applications. That isn’t a small increase. It points directly to a growing attack surface and a growing need for continuous monitoring.

Formalizing the Incident Response Plan

Eventually something will happen.

Maybe it is a ransomware event. Maybe it is a compromised account. Maybe it is a vendor-related incident.

Organizations that handle things well are not usually the ones who are making decisions for the very first time right when the crisis shows up.

In other words, an Incident Response Plan ought to lay out containment actions, the communication roles, escalation pathways, and the recovery steps, plus all reporting requirements before anything actually happens.

Preparation rarely feels urgent until the day it becomes critical.

Phase 4: Operationalizing the Framework and Testing Defenses

Cultivating an Organization-Wide Security Culture

Technology gets most of the attention. People still create many of the opportunities attackers exploit.

That doesn’t mean employees are the problem. It means they need support.

Security awareness should be role-specific and continuous. Finance teams face different risks than developers. Executives face different risks than customer support teams.

The objective isn’t fear. The objective is awareness.

Validation via Red Teaming and Penetration Testing

Many organizations spend months implementing controls and then never challenge them.

That is risky.

Security controls should be tested under realistic conditions. Red teaming exercises, penetration tests, tabletop scenarios, and independent assessments reveal weaknesses that dashboards often miss.

The gap between resilient organizations and struggling organizations often comes down to testing. According to the World Economic Forum’s Global Cybersecurity Outlook 2026, 44% of highly resilient organizations simulate cyber incidents with ecosystem partners. Among insufficiently resilient organizations, that figure drops to 16%.

That difference speaks for itself.

Building Long-Term Digital Resilience

A cybersecurity framework is not a destination.

It is a management system.

Threats evolve. Technology changes. Business priorities shift. New attack paths emerge. Security programs that remain frozen eventually become liabilities.

The organizations that stay ahead understand this reality. They continuously improve visibility, strengthen controls, test assumptions, and adapt to changing risks.

That mindset becomes even more important as AI reshapes security operations. McKinsey expects AI’s share of cybersecurity budgets to rise from approximately 4% today to 15% over the next three years. Whether organizations are ready or not, the security landscape is changing again.

The real challenge is not building a framework. Plenty of organizations can build one.

The challenge is building a framework that keeps evolving after everyone else stops paying attention.

The post How to Develop a Comprehensive Cybersecurity Framework for Modern Enterprise Protection? appeared first on ITDigest.

As organizations get overwhelmed with more and more data on vulnerabilities, threat intelligence, and compliance, security teams get increasingly frustrated in turning technical information into understandable business insights. Diligent’s new product is meant to address this problem by integrating cybersecurity details with overall enterprise risk and governance data in one single platform.

“Municipal security teams don’t have the luxury of piecing together risk from scattered scans and spreadsheets. We need up-to-date insight into how cybersecurity can best protect the services our community depends on,” said Dave Schultz, Risk Manager, Risk and Controls, City of Lethbridge. “An agentic solution that accelerates assessments and clearly connects threats and vulnerabilities to strategic priorities would be transformative in helping us make credible recommendations to leadership.”

Also Read: AttackIQ and Acumen Cyber Join Forces to Strengthen Continuous Cyber Defence Validation

The solution leverages AI to automate cyber risk assessments by analyzing threat, vulnerability, asset, and control data to generate risk scenarios and prioritization recommendations. Instead of just looking at the technical severity of issues, the platform considers the potential business impact of risks to help companies focus their efforts on the most important areas.

Other features are risk scoring at the asset level, a centralized remediation management, IT compliance functions integration that helps automate the control mapping and testing across different regulatory setups. Besides that, executives and board members get better insights into the organizational exposures through the platform’s dynamic dashboards that associate cyber risks with the business goals and This way, allow them to make well-informed decisions.

“Most security teams are drowning in vulnerability scans, threat feeds and control data, yet lacking the one thing the board keeps asking for: a clear picture of what is truly at risk for the business,” said Scott Bridgen, General Manager, Risk & Audit at Diligent. “Diligent Cyber Risk Management turns static risk registers and checkbox compliance into an AI-powered system of action, helping organizations prioritize the security decisions that matter and clarify the impact of AI, IT and cyber risk to management and the board.”

The launch expands Diligent’s growing portfolio of AI-powered governance, risk, and compliance solutions, reflecting the company’s broader strategy of embedding intelligent automation across the GRC lifecycle to improve decision-making, risk visibility, and operational resilience.

The post Diligent Unveils AI-Driven Cyber Risk Management Platform to Align Security with Business Priorities appeared first on ITDigest.

For years, software delivery moved at human speed, with changes written, reviewed, and tested by people. AI has removed that constraint—teams now produce more code than ever, increasingly with agents generating changes alongside humans. The systems responsible for testing, validating, and governing software haven’t kept pace, leaving teams with less visibility and control over what they are actually releasing.

Codecov provides critical visibility into how much of an application’s codebase has been tested and whether it meets the threshold required to ship safely. By embedding Codecov’s coverage intelligence directly into the Harness delivery pipeline, coverage signals can inform real-time release decisions before code is shipped to production.

Also Read: 42Crunch AI Coding Plugins Unlock Agentic DevSecOps for the Enterprise

“AI is dramatically accelerating code creation, but software delivery ultimately comes down to deciding what’s safe to release and when,” said Jyoti Bansal, co-founder and CEO of Harness. “By bringing Codecov into Harness, we can connect coverage data directly to delivery decisions so teams can move faster without sacrificing trust in what they ship. The future of software delivery will depend on turning signals like coverage into enforceable decisions that guide every release.”

With Codecov joining Harness, coverage data becomes part of the Harness Software Delivery Knowledge Graph, which connects deployment signals across the delivery lifecycle. Codecov adds a critical new layer to that intelligence: real-time coverage data tied directly to the repositories and changes flowing through the delivery pipeline.

By connecting coverage data with deployment history, security reachability analysis, incident telemetry, and change history, the platform can help teams understand release risk before code reaches production—giving them visibility into what changed, what was tested, and where risk may be accumulating as software delivery continues to accelerate.

Commitment to the Codecov Product and Community

Codecov is trusted by thousands of development teams and widely used across the open source ecosystem. Harness plans to continue investing in Codecov while preserving the experience teams rely on today.

The transition reflects a shared view that modern software delivery depends on strong alignment between development signals, operational visibility, and release governance. Harness plans to accelerate innovation in Codecov while retaining its brand and free access for open source projects.

“Sentry’s mission is to help developers see what’s breaking in production so they can fix it fast. This focus is what shapes every decision we make,” said Milin Desai, CEO, Sentry. “Codecov has become an important tool for thousands of engineering teams, and it deserves the investment and roadmap focus to match that. Coverage intelligence works best when it’s embedded in the delivery pipeline, and that’s exactly where Harness operates. We didn’t just find Codecov a home, we found it the right one.”

Ongoing Partnership Between Harness and Sentry

Harness and Sentry are committed to ensuring continuity for Codecov users while building long-term momentum for the product. The companies share a belief that the future of software delivery requires closer alignment between quality, reliability, and visibility.

Together, Harness and Sentry will continue supporting developers and engineering teams through integrations, developer community engagement, and shared efforts to advance the tools and practices needed for the AI-accelerated future of software delivery.

Source: PRNewswire

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Also Read: Kyndryl and Google Cloud Deepen Alliance to Power Enterprise AI and IT Modernization

The joint offering also delivers real-time observability, governance frameworks for compliance and auditability, federated architecture support for multi-business-unit deployments, and coding agent observability for tools such as Claude Code, Cursor, and Devin. The solution is specially designed for use by various industries that include finance, health care, and insurance industries where transparency, traceability, and adherence to regulations are key. The collaboration between Bitwise and HoneyHive seeks to ensure that businesses undergo successful AI transformation with ease through the management and deployment of their AI systems.

Read More: Bitwise and HoneyHive Announce Strategic Partnership to Enable Scalable, Governed Enterprise AI

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