AI Security Threat Intelligence Hub

Security programs were built around a threat model that assumed clear boundaries: data lives in databases, code lives in code repositories, and trusted users are separated from untrusted external parties by authentication and access controls. AI assistants — and agentic AI systems in particular — have been systematically eroding all three of those assumptions since 2024, and most enterprise security teams have not caught up.

Tech Sentinel covers cybersecurity news with an engineer’s filter. That means we go deeper than vendor press releases and incident headlines to explain what actually happened, what the technical mechanism was, and what the security architecture implication is. For AI security specifically, that means tracking the incidents that reveal how the threat model is changing, the vulnerabilities in the AI infrastructure that enterprises have already deployed, and the workforce and organizational dynamics that determine whether security programs can actually respond.

This hub page indexes the most useful coverage on Tech Sentinel organized around the themes that matter most for enterprise security teams dealing with AI in 2026. The landscape is moving fast. Use this index as a stable reference point and return to it as new coverage is added.

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The Evolving AI Threat Model

Understanding how AI systems change the attack surface — not just as individual vulnerabilities, but as a structural shift in what security teams need to defend.

AI Agents Are Rewriting the Threat Model, and Most Security Teams Aren’t Ready The foundational analysis. AI coding assistants and autonomous “co-workers” blur the line between data and code, trusted user and insider threat. The Clinejection attack, the FortiGate campaign, and the OpenClaw exposure wave, analyzed together as a pattern: autonomous AI systems collapse the boundaries — between user and system, between data and code, between trusted tool and attack surface — that enterprise security architecture was built to protect. Covers what “prompt injection at the boundary” means as a security engineering problem and what organizational responses are actually working. Required reading for any security architect evaluating AI assistant deployment.

Generative AI Risks: A Practitioner’s Guide to What Matters The framework-level view. What NIST AI 600-1, the OWASP LLM Top 10, and Cisco’s State of AI Security report each say about prompt injection, data leakage, confabulation, supply chain poisoning, and agentic exposure, and where the three diverge. Start here if you need a risk register rather than a single vulnerability writeup.

LLM Security Risks: The OWASP Top 10 and How to Defend The category-by-category breakdown of LLM01 through LLM10, from prompt injection and system prompt leakage to excessive agency and unbounded consumption, with the controls that measurably reduce exposure and five priorities ordered by impact.

Machine Learning Security: Key Threats, Attacks, and Defenses The non-generative half of the problem. Evasion, poisoning, model extraction, membership inference, and backdoors against the classifiers already running in fraud and detection pipelines, mapped to the NIST AI 100-2 and NCSC taxonomies.

Deepfake Cybersecurity: How AI Voice Cloning Reshapes Fraud Where generative models become the attacker’s tooling rather than the target. Voice cloning from seconds of audio, video-conference impersonation, the state of detection science, and the procedural controls that survive contact with a convincing synthetic executive.

AI Fraud Detection: How It Works and Where It Fails The defensive counterpart. How supervised classifiers, anomaly detection, graph neural networks, and behavioral biometrics stack in production, how attackers probe and evade them, and why cross-institutional data fragmentation caps the whole stack.

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Enterprise AI Vulnerabilities

Specific vulnerabilities in the AI systems enterprises have already deployed.

ChatGPT Security: Key Risks, Real Vulnerabilities, and Enterprise Controls That Work A security-focused breakdown of ChatGPT risk in enterprise deployments. Covers documented vulnerabilities — DNS-based data exfiltration, command injection in Codex, credential theft patterns — alongside the enterprise controls that actually reduce exposure. Practical for security teams developing AI acceptable use policies or evaluating ChatGPT Enterprise deployments.

OpenAI Security: Bug Bounty Programs and Documented Incidents The vendor-level record behind those deployments: two public bug bounty programs, a coordinated disclosure and CVE assignment policy, and the confirmed incidents from 2023 through 2026, including the July 2026 agentic sandbox escape into Hugging Face infrastructure. Useful when vendor security claims need to be checked against the public record.

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Workforce and Organizational Dynamics

The human infrastructure of security programs — who does the work, what they’re experiencing, and how AI changes the operating model.

Cybersecurity Burnout Is a Structural Problem, Not a Personal One A Sophos survey of 5,000 practitioners found 76% experiencing burnout. This post argues that’s a systems problem: the alert volume and staffing gaps that cause burnout are getting worse as AI accelerates vulnerability discovery, while the organizational model that creates those gaps remains unchanged. Relevant for security leadership thinking about team capacity as AI-generated vulnerability research increases the pace of disclosure.

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What this site covers

How Tech Sentinel Filters Cybersecurity News Tech Sentinel’s scope: cybersecurity news coverage with an engineer’s filter. What we publish, what we don’t, and how to calibrate the signal-to-noise ratio.

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Cross-Site Reading

Tech Sentinel covers news and threat intelligence. For technical deep dives on offensive AI security techniques, see aisec.blog. For AI incident tracking and CVE coverage, see AI Alert. For defensive engineering and guardrails, see GuardML.