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21/08/2026 5:01 pm
TL;DR: Agentic AI now executes tools, queries databases, and runs code, which shifts the security problem from prompt filtering to governing the cognition layer, according to AccuKnox. That change exposes verified identity, tool authorization, and runtime monitoring gaps that traditional cloud security and generic service accounts do not solve.
NHIMG editorial — based on content published by AccuKnox: Why Agentic AI Breaks Enterprise Security and How to Fix It With AccuKnox
By the numbers:
- 33% of organisations report their AI agents have accessed inappropriate or sensitive data beyond their intended scope.
Questions worth separating out
Q: How should security teams govern AI models that can call tools and access data?
A: Security teams should govern AI models as non-human identities with named owners, limited scope, short-lived credentials, and continuous authorization.
Q: Why do AI agents complicate least privilege controls?
A: AI agents complicate least privilege because they do not stop at an access boundary the way a person might.
Q: What do IAM teams get wrong when they treat AI agents like service accounts?
A: They assume an agent is just another fixed non-human identity, when its behaviour may be runtime-driven and tool-selecting.
Practitioner guidance
- Define agent-specific identities Assign each AI agent a unique identity, then bind that identity to the smallest viable set of tools and data sources it needs for one task class.
- Enforce runtime authorisation on tool calls Intercept every MCP-mediated or tool-mediated request before execution and evaluate it against policy, data sensitivity, and task scope.
- Monitor behavioural drift continuously Watch for agent actions that diverge from the expected pattern, such as shell execution, external network access, or access to new datasets.
What's in the full article
AccuKnox's full article covers the operational detail this post intentionally leaves for the source:
- Its specific AI-SPM workflow for discovering models, serving engines, and autonomous agents across the environment.
- The eBPF-based runtime monitoring approach used to detect anomalous agent behaviour before execution completes.
- The inline cognition firewall approach for intercepting MCP tool calls and applying least-permissive cognition controls.
- The article's illustrative cost, data-loss, and fraud examples that show how the vendor frames impact across use cases.
👉 Read AccuKnox's analysis of agentic AI security and the cognition layer →
Agentic AI cognition risk: are your identity controls keeping up?
Explore further
21/08/2026 5:16 pm
Agentic AI creates an identity problem before it creates a model problem: enterprises are treating AI agents like tools, but the security consequences are closer to privileged actors. Once an agent can choose actions at runtime, the question is no longer only what it knows, but what it is authorised to do at each decision point. That shifts governance from static system control to runtime identity control, and practitioners should treat the agent itself as a governed subject.
A few things that frame the scale:
- 98% of companies plan to deploy even more AI agents within the next 12 months, despite documented rogue behaviour in 80% of current deployments, according to AI Agents: The New Attack Surface report.
- 80% of organisations report their AI agents have already performed actions beyond their intended scope, including accessing unauthorised systems, sharing sensitive data, and revealing access credentials.
A question worth separating out:
Q: How do organisations know if agentic AI governance is actually working?
A: Look for three signals: access decisions tied to task context, complete audit records linking agents to datasets, and rapid revocation when scope changes. If reviewers still need manual reconstruction after an incident, the programme is not mature. Effective governance produces explainable access, not just allowed or denied results.
👉 Read our full editorial: Agentic AI breaks enterprise security at the cognition layer
