Topic starter
09/07/2026 1:40 pm
TL;DR: Real enterprise incident data is shaping the OWASP Top 10 for Agentic Applications because theoretical models miss recurring threat patterns such as goal hijacking, rogue agents, cascading failures, and supply chain attacks, according to Zenity. The governance gap is that AI security frameworks built on static assumptions cannot keep pace with autonomous behaviour, where detection, accountability, and response must be designed around runtime action rather than policy intent.
Editorial analysis by NHI Mgmt Group, based on content published by Zenity: “Seeing Thousands of Real Incidents Means I Have No Choice But to Share What I Know”.
Key questions
Q: What breaks when agentic AI relies on static IAM assumptions?
A: Static IAM breaks when the system can decide new steps at runtime.
Q: Why do goal hijacking and rogue agents matter to IAM teams?
A: They matter because they turn identity into an execution problem, not just an access problem.
Q: How can security teams use incident data to improve agent governance?
A: They should convert repeated incidents into control categories, detection signals, and review criteria that reflect how agents actually fail in production.
Practitioner guidance
- Inventory agent decision points Map where an AI agent can change goals, choose tools, or continue execution without a human approval gate.
- Classify recurring incident patterns Group incidents by goal hijacking, rogue behaviour, cascading impact, and supply chain influence so that detection logic reflects observed attack patterns rather than generic AI risk labels.
- Review connector and tool provenance Track which external services, plugins, and integrations can alter agent behaviour, and treat unreviewed changes as security events with governance impact.
Bottom line: Agentic AI frameworks are becoming more useful when they are built from repeated enterprise incidents rather than speculative threat lists.
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09/07/2026 1:44 pm
Incident data is now the only credible source for agentic AI security frameworks: The article's central claim is that meaningful frameworks for agentic systems are being built from repeated real-world failures, not from abstract threat modeling alone. That matters because agentic behaviour changes too quickly for static assumptions to stay valid for long. Security teams should expect framework design to follow observed abuse patterns rather than vendor narratives.
A few things that frame the scale:
- Only 23% of IT leaders were very confident in their organisation's ability to manage security and governance for GenAI deployments, according to a 2025 Gartner survey of 360 IT leaders.
A question worth separating out:
Q: What is the difference between agentic supply chain risk and ordinary software supply chain risk?
A: Ordinary software supply chain risk focuses on code integrity and trusted dependencies. Agentic supply chain risk also covers components that shape behaviour at runtime, such as tools, connectors, prompts, and orchestration logic. In other words, the supply chain is not only what the software is, but what the agent is allowed to become.
👉 Read our full editorial: Agentic AI security frameworks are being built from real incidents