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10/06/2026 12:26 am
TL;DR: One in eight reported AI breaches is now linked to agentic systems, while 76% of organisations cite shadow AI as a definite or probable problem and 31% do not know whether they experienced an AI security breach in the past year, according to HiddenLayer’s 2026 AI Threat Landscape Report based on a survey of 250 IT and security leaders. The governance gap is no longer theoretical: controls built for static software cannot reliably contain systems that browse, execute, and act at runtime.
NHIMG editorial — based on content published by HiddenLayer: HiddenLayer Releases the 2026 AI Threat Landscape Report, Spotting the Rise of Agentic AI and the Expanding Attack Surface of Autonomous Systems
By the numbers:
- Shadow AI is now a definite or probable problem for over 3 in 4 organizations, or 76%.
- 31% of organizations do not know whether they experienced an AI security breach in the past 12 months.
- 93% of respondents continue to rely on open repositories for innovation.
Questions worth separating out
Q: What breaks when AI agents are governed like normal applications?
A: When AI agents are governed like normal applications, the programme usually focuses on deployment and monitoring, but not on runtime authority.
Q: Why do AI agents complicate zero trust and least privilege?
A: AI agents complicate zero trust and least privilege because their effective privilege can change during execution.
Q: How do security teams know if shadow AI is actually under control?
A: Security teams know shadow AI is under control when they can inventory every agent, model workflow, and tool connection, then map each one to an owner and access scope.
Practitioner guidance
- Inventory every agentic system as a governed identity Capture agents, embedded models, tool connectors, and API-linked workflows in the same inventory used for other non-human identities.
- Constrain agent tool access by task and environment Separate browsing, file access, code execution, and workflow triggers into distinct permissions so a single compromised agent cannot move freely across the stack.
- Add runtime policy to agent execution paths Enforce policy at the point where the agent calls tools, not only at the prompt or interface layer.
What's in the full report
HiddenLayer's full report covers the operational detail this post intentionally leaves for the source:
- Survey breakdowns that show how 250 IT and security leaders are prioritising AI risk across production environments
- The report's AI security platform modules and how HiddenLayer describes discovery, supply chain, simulation, and runtime coverage
- The report's findings on ownership conflict, budget allocation, and disclosure behaviour across respondent groups
- The full trend discussion on how agentic, reasoning, and edge models are reshaping the attack surface
👉 Read HiddenLayer’s 2026 AI Threat Landscape Report on agentic AI risk →
Agentic AI breach risk is rising fast, but are controls keeping up?
Explore further
11/06/2026 2:10 am
Agentic AI turns access governance into behaviour governance. Traditional IAM assumes the identity is relatively stable, and that the main question is whether the right access was assigned. That assumption breaks when the actor can choose tools, sequence actions, and continue operating after context changes. The field needs to stop treating agent governance as a wrapper around the model and start treating it as a runtime identity problem.
A few things that frame the scale:
- 92% agree governing AI agents is critical to enterprise security, yet only 44% have implemented any policies to do so, according to AI Agents: The New Attack Surface report.
- 33% of organisations report their AI agents have accessed inappropriate or sensitive data beyond their intended scope, which shows scope drift is already operational.
A question worth separating out:
Q: What should organisations do first after discovering unmanaged AI agents?
A: Organisations should first isolate the credentials, tool connections, and data paths attached to the unmanaged agent before expanding use further. Then they should decide whether the system is approved, remediated, or retired. That sequence limits hidden authority and reduces the chance of an unseen workflow becoming an attack path.
👉 Read our full editorial: AI threat landscape report shows agentic systems widening AI risk
12/06/2026 3:45 am
Agentic AI turns access governance into behaviour governance. Traditional IAM assumes the identity is relatively stable, and that the main question is whether the right access was assigned. That assumption breaks when the actor can choose tools, sequence actions, and continue operating after context changes. The field needs to stop treating agent governance as a wrapper around the model and start treating it as a runtime identity problem.
A few things that frame the scale:
- 92% agree governing AI agents is critical to enterprise security, yet only 44% have implemented any policies to do so, according to AI Agents: The New Attack Surface report.
- 33% of organisations report their AI agents have accessed inappropriate or sensitive data beyond their intended scope, which shows scope drift is already operational.
A question worth separating out:
Q: What should organisations do first after discovering unmanaged AI agents?
A: Organisations should first isolate the credentials, tool connections, and data paths attached to the unmanaged agent before expanding use further. Then they should decide whether the system is approved, remediated, or retired. That sequence limits hidden authority and reduces the chance of an unseen workflow becoming an attack path.
👉 Read our full editorial: AI threat landscape report shows agentic systems widening AI risk
