Security & IT News

Over the past year, I have had conversations with security leaders across a variety of disciplines, and the energy around AI is undeniable. Organizations are moving fast, and security teams are rising to meet the moment. Time and again, the question comes back to the same thing: “We’re adopting AI fast, how do we make sure our security keeps pace?” Explore the updated Microsoft Zero Trust Workshop and Assessment It’s the right question, and it’s the one we’ve been working to answer by updating the tools and guidance you already rely on. We’re announcing Microsoft’s approach to Zero Trust for AI (ZT4AI). Zero Trust for AI extends proven Zero Trust principles to the full AI lifecycle—from data ingestion and model training to deployment and agent behavior. Today, we’re releasing a new set of tools and guidance to help you move forward with confidence: A new AI pillar in the Zero Trust Workshop . Updated Data and Networking pillars in the Zero Trust Assessment tool. A new Zero Trust reference architecture for AI. Practical patterns and practices for securing AI at scale. Here’s what’s new and how to use it. Why Zero Trust principles must extend to AI AI systems don’t fit neatly into traditional security models. They introduce new trust boundaries—between users and agents, models and data, and humans and automated decision-making. As organizations adopt autonomous and semi-autonomous AI agents, a new class of risk emerges: agents that are overprivileged, manipulated, or misaligned can act like “double agents,” working against the very outcomes they were built to support. Watch the video: AI with Zero Trust Security By applying three foundational principles of Zero Trust to AI: Verify explicitly —Continuously evaluate the identity and behavior of AI agents, workloads, and users. Apply least privilege —Restrict access to models, prompts, plugins, and data sources to only what’s needed. Assume breach —Design AI systems to be resilient to prompt injection, data poisoning, and lateral movement. These aren’t new principles. What’s new is how we apply them systematically to AI environments. A unified journey: Strategy → assessment → implementation The most common challenge we hear from security leaders and practitioners is a lack of a clear, structured path from knowing what to do to doing it. That’s what Microsoft’s approach to Zero Trust for AI is designed to solve—to help you get to next steps and actions, quickly. Zero Trust Workshop—now with an AI pillar Building on last year’s announcement , the Zero Trust Workshop has been updated with a dedicated AI pillar, now covering 700 security controls across 116 logical groups and 33 functional swim lanes. It is scenario-based and prescriptive, designed to move teams from assessment to execution with clarity and speed. The workshop helps organizations: Align security, IT, and business stakeholders on sha

Adoption of Generative AI (GenAI) and agentic AI has accelerated from experimentation into real enterprise deployments. What began with copilots and chat interfaces has quickly evolved into powerful business systems that autonomously interact with sensitive data, call external APIs, connect to consequential tools, initiate workflows, and collaborate with other agents across enterprise environments. As these AI systems become core infrastructure, establishing clear, continuous visibility into how these systems behave in production can help teams detect risk, validate policy adherence, and maintain operational control. Observability is one of the foundational security and governance requirements for AI systems operating in production. Yet many organizations don’t understand the critical importance of observability for AI systems or how to implement effective AI observability. That mismatch creates potential blind spots at precisely the moment when visibility matters most. In February, Microsoft Corporate Vice President and Deputy Chief Information Security Officer, Yonatan Zunger, blogged about expanding Microsoft’s Secure Development Lifecycle (SDL) to address AI-specific security concerns. Today, we continue the discussion with a deep dive into observability as a necessity for the secure development of GenAI and agentic AI systems. For additional context, read the Secure Agentic AI for Your Frontier Transformation blog that covers how to manage agent sprawl, strengthen identity controls, and improve governance across your tenant. Observability for AI systems In traditional software, client apps make structured API calls and backend services execute predefined logic. Because code paths follow deterministic flows, traditional observability tools can surface straightforward metrics like latency, errors, and throughput to track software performance in production. GenAI and agentic AI systems complicate this model. AI systems are probabilistic by design and make complex decisions about what to do next as they run. This makes relying on predictable finite sets of success and failure modes much more difficult. We need to evolve the types of signals and telemetry collected so that we can accurately understand and govern what is happening in an AI system. Consider this scenario: an email agent asks a research agent to look up something on the web. The research agent fetches a page containing hidden instructions and passes the poisoned content back to the email agent as trusted input. The email agent, now operating under attacker influence, forwards sensitive documents to unauthorized recipients, resulting in data exfiltration. In this example, traditional health metrics stay green: no failures, no errors, no alerts. The system is working exactly as designed… except a boundary between untrusted external content and trusted agent context has been compromised. This illustrates how AI systems require a unique approach to observability. Without insights

As organizations adopt AI, security and governance remain core primitives for safe AI transformation and acceleration. After all, data leaders are aware of the notion that: Your AI is only as good as your data. Organizations are skeptical about AI transformation due to concerns of sensitive data oversharing and poor data quality. In fact, 86% of organizations lack visibility into AI data flows, operating in darkness about what information employees share with AI systems [1] . Compounding on this challenge, about 67% of executives are uncomfortable using data for AI due to quality concerns [2]. The challenges of data oversharing and poor data quality requires organizations to solve these issues seamlessly for the safe usage of AI. Microsoft Purview offers a modern, unified approach to help organizations secure and govern data across their entire data estate, in particular best in class integrations with M365, Microsoft Fabric, and Azure data estates, streamlining oversight and reducing complexity across the estate. At FabCon Atlanta, we’re announcing new Microsoft Purview innovations for Fabric to help seamlessly secure and confidently activate your data for AI transformation. These updates span data security and data governance, granting Fabric users to both Discover risks and prevent data oversharing in Fabric Improve governance processes and data quality across their data estate 1. Discover risks and prevent data oversharing in Fabric As data volume increases with AI usage, Microsoft Purview secures your data with capabilities such as Information Protection, Data Loss Prevention (DLP), Insider Risk Management (IRM), and Data Security Posture Management (DSPM). These capabilities work together to secure data throughout its lifecycle and now specifically for your Fabric data estate. Here are a few new Purview innovations for your Fabric estate: Microsoft Purview DLP policies to prevent data leakage for Fabric Warehouse and KQL/SQL DBs Now generally available, Microsoft Purview DLP policies allow Fabric admins to prevent data oversharing in Fabric through policy tip triggering when sensitive data is detected in assets uploaded to Warehouses. Additionally, in preview, Purview DLP enables Fabric admins to restrict access to assets with sensitive data in KQL/SQL DBs and Fabric Warehouses to prevent data oversharing. This helps admins limit access to sensitive data detected in these data sources and data stores to just asset owners and allowed collaborators. These DLP innovations expand upon the depth and breadth of existing DLP policies to ensure sensitive data in Fabric is protected. Figure 1. DLP restrict access preventing data oversharing of customer information stored in a KQL database. Microsoft Purview Insider Risk Management (IRM) indicators for Lakehouse, IRM data theft quick policy for Fabric, and IRM pay-as-you-go usage report for Fabric Microsoft Purview Insider Risk Management is now generally available for Microsoft Fabric extending its

Microsoft Corp. today pushed security updates to fix at least 77 vulnerabilities in its Windows operating systems and other software. There are no pressing “zero-day” flaws this month (compared to February’s five zero-day treat), but as usual some patches may deserve more rapid attention from organizations using Windows. Here are a few highlights from this month’s Patch Tuesday. Image: Shutterstock, @nwz. Two of the bugs Microsoft patched today were publicly disclosed previously. CVE-2026-21262 is a weakness that allows an attacker to elevate their privileges on SQL Server 2016 and later editions. “This isn’t just any elevation of privilege vulnerability, either; the advisory notes that an authorized attacker can elevate privileges to sysadmin over a network,” Rapid7’s Adam Barnett said. “The CVSS v3 base score of 8.8 is just below the threshold for critical severity, since low-level privileges are required. It would be a courageous defender who shrugged and deferred the patches for this one.” The other publicly disclosed flaw is CVE-2026-26127 , a vulnerability in applications running on .NET . Barnett said the immediate impact of exploitation is likely limited to denial of service by triggering a crash, with the potential for other types of attacks during a service reboot. It would hardly be a proper Patch Tuesday without at least one critical Microsoft Office exploit, and this month doesn’t disappoint. CVE-2026-26113 and CVE-2026-26110 are both remote code execution flaws that can be triggered just by viewing a booby-trapped message in the Preview Pane. Satnam Narang at Tenable notes that just over half (55%) of all Patch Tuesday CVEs this month are privilege escalation bugs, and of those, a half dozen were rated “exploitation more likely” — across Windows Graphics Component, Windows Accessibility Infrastructure, Windows Kernel, Windows SMB Server and Winlogon. These include: – CVE-2026-24291 : Incorrect permission assignments within the Windows Accessibility Infrastructure to reach SYSTEM (CVSS 7.8) – CVE-2026-24294 : Improper authentication in the core SMB component (CVSS 7.8) – CVE-2026-24289 : High-severity memory corruption and race condition flaw (CVSS 7.8) – CVE-2026-25187 : Winlogon process weakness discovered by Google Project Zero (CVSS 7.8). Ben McCarthy , lead cyber security engineer at Immersive , called attention to CVE-2026-21536 , a critical remote code execution bug in a component called the Microsoft Devices Pricing Program. Microsoft has already resolved the issue on their end, and fixing it requires no action on the part of Windows users. But McCarthy says it’s notable as one of the first vulnerabilities identified by an AI agent and officially recognized with a CVE attributed to the Windows operating system. It was discovered by XBOW , a fully autonomous AI penetration testing agent. XBOW has consistently ranked at o

CVSSv3 Score: 6.7 An Improper Neutralization of Special Elements used in an OS Command ('OS Command Injection') vulnerability [CWE-78] in FortiSandbox Cloud WEB UI may allow a privileged attacker with super-admin profile and CLI access to execute unauthorized code or commands via crafted HTTP requests. Revised on 2026-03-10 00:00:00

CVSSv3 Score: 7.0 A Stack-based Buffer Overflow vulnerability [CWE-121] in FortiManager fgtupdates service may allow a remote unauthenticated attacker to execute unauthorized commands via crafted requests, if the service is enabled. The success of the attack depends on the ability to bypass the stack protection mechanisms. Revised on 2026-03-10 00:00:00

Microsoft today released updates to fix more than 50 security holes in its Windows operating systems and other software, including patches for a whopping six “zero-day” vulnerabilities that attackers are already exploiting in the wild. Zero-day #1 this month is CVE-2026-21510 , a security feature bypass vulnerability in Windows Shell wherein a single click on a malicious link can quietly bypass Windows protections and run attacker-controlled content without warning or consent dialogs. CVE-2026-21510 affects all currently supported versions of Windows. The zero-day flaw CVE-2026-21513 is a security bypass bug targeting MSHTML , the proprietary engine of the default Web browser in Windows. CVE-2026-21514 is a related security feature bypass in Microsoft Word. The zero-day CVE-2026-21533 allows local attackers to elevate their user privileges to “SYSTEM” level access in Windows Remote Desktop Services . CVE-2026-21519 is a zero-day elevation of privilege flaw in the Desktop Window Manager (DWM), a key component of Windows that organizes windows on a user’s screen. Microsoft fixed a different zero-day in DWM just last month . The sixth zero-day is CVE-2026-21525 , a potentially disruptive denial-of-service vulnerability in the Windows Remote Access Connection Manager , the service responsible for maintaining VPN connections to corporate networks. Chris Goettl at Ivanti reminds us Microsoft has issued several out-of-band security updates since January’s Patch Tuesday. On January 17, Microsoft pushed a fix that resolved a credential prompt failure when attempting remote desktop or remote application connections. On January 26, Microsoft patched a zero-day security feature bypass vulnerability ( CVE-2026-21509 ) in Microsoft Office . Kev Breen at Immersive notes that this month’s Patch Tuesday includes several fixes for remote code execution vulnerabilities affecting GitHub Copilot and multiple integrated development environments (IDEs), including VS Code , Visual Studio , and JetBrains products. The relevant CVEs are CVE-2026-21516 , CVE-2026-21523 , and CVE-2026-21256 . Breen said the AI vulnerabilities Microsoft patched this month stem from a command injection flaw that can be triggered through prompt injection, or tricking the AI agent into doing something it shouldn’t — like executing malicious code or commands. “Developers are high-value targets for threat actors, as they often have access to sensitive data such as API keys and secrets that function as keys to critical infrastructure, including privileged AWS or Azure API keys,” Breen said. “When organizations enable developers and automation pipelines to use LLMs and agentic AI, a malicious prompt can have significant impact. This does not mean organizations should stop using AI. It does mean developers should understand the risks, teams should clearly identify which systems and workflows have access to AI agents, and least-privile

CVSSv3 Score: 5.3 An Exposure of Sensitive Information to an Unauthorized Actor vulnerability [CWE-200] in FortiOS SSL-VPN may allow a remote unauthenticated attacker to bypass the patch developed for the symbolic link persistency mechanism observed in some post-exploit cases, via crafted HTTP requests. An attacker would need first to have compromised the product via another vulnerability, at filesystem level. Revised on 2026-03-12 00:00:00