Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

Understanding Context Windows in AI-Powered Security Operations

Understanding Context Windows in AI-Powered Security Operations

Jul 24, 2026 By SecuritySenses In SecuritySenses
Your security operations team now relies on AI agents to detect threats, triage alerts, and accelerate incident investigation. These agents analyze signals across your environment to identify suspicious behavior that humans might miss, and they respond faster than any manual process could. But they operate under a fundamental constraint that most security teams overlook: context window limitations that directly impact investigation quality and threat visibility.
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Corelight

You can't govern what you can't see: Detecting shadow AI on your network

Jul 22, 2026 By Tim Chiu In Corelight
AI adoption inside the enterprise didn't ask for permission. It arrived through browser tabs, code editors, and meeting transcription bots, quietly stitching itself into daily workflows long before security teams could write policy around it. The result is a familiar story with a new villain, a sprawling, unmanaged attack surface that lives in your network traffic but nowhere in your asset inventory. We call it shadow AI, and it's the blind spot you didn't plan for or budget for.
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teramind

7 AI Governance Tools for Shadow AI Detection

Jul 13, 2026 By Teramind In Teramind
AI adoption has accelerated faster than most organizations’ ability to manage it. Security and compliance teams are now responsible for overseeing machine learning models, large language models (LLMs), agentic AI systems, and shadow AI — often with frameworks and processes that weren’t built for any of it. The gap between deploying AI and governing it responsibly is where risk lives. AI governance tools exist to close that gap.
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cyberhaven

Agentic AI Governance Requires a New Enforcement Model

Jul 13, 2026 By Cyberhaven In Cyberhaven
AI has swiftly shifted from a browser-based chat interface to an autonomous actor operating within enterprise environments. Agents run locally on endpoints, inherit employee permissions, access sensitive data in bulk, and execute multi-step workflows with no human approving each step. That shift fundamentally changes the enforcement surface. The governance programs most organizations have built were designed for a different model: one user, one prompt, one decision.
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CrowdStrike

Why AI Governance Without Guardrails Is Theater

Jul 9, 2026 By CrowdStrike In CrowdStrike
AI governance is a key enterprise concern. Organizations are assembling councils, publishing principles, rolling out “approved AI tools” lists, and asking employees to opt in to acceptable use policies. In most enterprises, however, the reality is that AI is already widely embedded in employees' daily work, often outside sanctioned channels and oversight. The visibility and control mechanisms needed to govern AI use are immature or nonexistent.
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mend

Best AI Governance Platforms for Enterprises: Top 6 in 2026

Jul 7, 2026 By Tiffany Jennings In Mend
AI governance platforms provide enterprises with centralized oversight to manage AI risks, ensure regulatory compliance, and automate policy enforcement across the AI lifecycle. Leading solutions include security-oriented tools like Mend.io, HiddenLayer, and Prompt Security, as well as end-to-end governance platforms like IBM watsonx.governance and Microsoft Purview.
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mend

9-Step AI Governance Implementation Strategy and the Solutions to Know

Jul 2, 2026 By Tiffany Jennings In Mend
TL;DR: AI governance solutions help organizations inventory, secure, and monitor AI systems. Best for AI security and shadow AI: Mend AI; enterprise risk and compliance: Credo AI and IBM watsonx.governance; model monitoring: Fiddler AI. Effective AI governance implementation involves establishing a cross-functional committee, compiling an AI bill of materials (AI-BOM) to identify risks, and implementing policies based on frameworks like NIST AI RMF.
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Why Data Governance Matters When Adopting AI-Driven Student Enrollment Solutions

Why Data Governance Matters When Adopting AI-Driven Student Enrollment Solutions

Jun 25, 2026 By SecuritySenses In SecuritySenses
Schools, colleges, and universities are under constant pressure to make enrollment faster, simpler, and more accurate. This is why so many institutions are now turning to student enrollment solutions powered by artificial intelligence. These tools can predict applicant behavior, automate paperwork, flag incomplete forms, and even help admissions teams identify which students are likely to enroll. The appeal is obvious. But there is a part of this shift that often gets overlooked in the excitement around automation, and that is data governance.
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jfrog

Why Uniform Governance Fails with Enterprise AI Agents (And How to Fix It)

Jun 24, 2026 By Rami Pinku In JFrog
As organizations aggressively shift from static Large Language Model (LLM) chatbots to fully dynamic, autonomous AI agents (e.g. systems designed to plan workflows, call APIs, write runtime code, and modify enterprise databases), traditional compliance and governance frameworks are hitting a breaking point. A landmark press release from Gartner highlights a critical systemic risk: treating AI agent governance as a monolithic, one-size-fits-all policy guarantees project failure.
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cyberhaven

Data Governance vs. Data Security

Jun 24, 2026 By Cyberhaven In Cyberhaven
Most organizations treat data security and data governance as parallel tracks managed by separate teams with separate tooling. Security owns the controls; governance owns the policies. The two programs rarely share a roadmap, and the gaps between them are where data risk actually lives. Governance without security enforcement leaves policy on paper. Security without governance context produces alerts without the underlying understanding of what the data is, who owns it, or why it matters.
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