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02/08/2026 11:36 am
TL;DR: AI agents are already in 69% of organisations, yet only 21% have a complete inventory of agents and MCP connections and 79% lack governance policies for permissions and monitoring, according to Akto’s 2026 analysis. Security teams now need AI-specific visibility, least-privilege controls, and runtime governance because traditional API security does not contain agentic behaviour.
NHIMG editorial — based on content published by Akto: AI Security: Risks, Threats & Best Practices for LLMs (2026)
By the numbers:
- 69% of organizations have already deployed AI agents in real-world environments, rapidly expanding the enterprise attack surface.
- Only 21% of enterprises maintain a complete inventory of AI agents and MCP connections, creating major visibility gaps.
- 79% of organizations lack governance policies for AI agent permissions and monitoring, increasing the risk of unauthorized access and untracked AI behavior.
Questions worth separating out
Q: How should security teams govern AI agents that can access enterprise systems?
A: Security teams should govern AI agents as non-human identities with explicit ownership, scoped privileges, and continuous monitoring.
Q: Why do AI agents create more risk than traditional automation?
A: AI agents create more risk because they can interpret context, choose actions, and invoke tools autonomously.
Q: What do organisations get wrong about prompt injection?
A: They often treat it as a purely content-filtering problem.
Practitioner guidance
- Implement task-scoped agent identities Give each AI agent its own service identity, minimal permissions, and separate credentials so access can be revoked without affecting unrelated workflows.
- Build a live AI asset inventory Track every model, plugin, MCP connection, and external data source in a continuously updated register so shadow AI does not sit outside governance.
- Enforce runtime input and tool-call controls Inspect prompts, retrieved documents, and outbound API calls before execution, and block actions that exceed the task’s approved scope.
What's in the full article
Akto's full blog covers the operational detail this post intentionally leaves for the source:
- A deeper breakdown of AI-SPM discovery steps across models, plugins, prompts, and integrations
- Specific runtime control examples for prompt injection detection, output filtering, and tool-call validation
- Practical guidance on assigning service identities and audit trails to AI agents in production
- Implementation-oriented discussion of governance, compliance, and accountability mapping for AI systems
👉 Read Akto's analysis of AI security risks, threats, and best practices for LLMs →
AI security threat landscape: are your controls keeping up?
Explore further
02/08/2026 12:50 pm
AI agent governance is now an identity problem, not only a model-risk problem. Once agents can call tools, read data, and trigger workflows, the real control boundary becomes who or what is allowed to act. That changes the center of gravity for IAM, PAM, and NHI programmes. The organisations that treat agents as credentialed actors will govern them more effectively than those that treat them as chat interfaces.
A question worth separating out:
Q: Which frameworks should teams use to evaluate AI security controls and accountability?
A: Use NIST AI RMF for governance, OWASP guidance for common AI attack patterns, MITRE ATLAS for adversarial techniques, and ISO 27001 where enterprise control mapping is needed. The framework should help teams prioritise, test, and evidence controls, not replace validation against the actual model, data, and agent workflows.
👉 Read our full editorial: AI security risk is outpacing controls for LLMs and agents
