Topic starter
02/08/2026 11:36 am
TL;DR: Agent adoption has reached 69% of organisations, with only 21% keeping a complete inventory of agents and MCP connections and 79% lacking governance policies for permissions and monitoring, Akto found. Runtime authorisation and continuous oversight become critical when AI systems can trigger real-world actions without a full estate record.
Editorial analysis by NHI Mgmt Group, based on content published by Akto: “AI Security: Risks, Threats & Best Practices for LLMs (2026)”.
By the numbers:
- 69% of organisations 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.
Key questions
Q: When should organisations prioritise inventory over broader AI governance controls?
A: Inventory should come first because every downstream control depends on knowing what exists.
Q: What do security teams get wrong about AI agent permissions?
A: Teams often assume that permissions are safe if the agent was approved at deployment.
Q: What breaks when AI agents are treated like standard human users?
A: You lose visibility into effective permissions, expected behaviour, and real blast radius.
Practitioner guidance
- Implement complete AI asset inventory Catalog every model, agent, plugin, integration, and MCP connection, then assign an owner and data access scope to each one.
- Issue task-scoped identities for agents Give each AI agent a distinct service identity with minimum permissions, separate from human or shared application credentials.
- Enforce runtime policy at inference time Inspect prompts, retrieved content, tool calls, and outputs before side effects occur, and block actions that exceed policy.
Bottom line: AI security risk is now driven by systems that interpret context and act, not just by models that generate output.
Glossary terms: Agentic AI Security, AI Security Posture Management, Prompt Injection (Agentic), Shadow AI
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This topic was modified 2 days ago by NHI Mgmt Group
02/08/2026 12:50 pm
AI security now has an identity problem: the moment agents can call tools, reach data, or trigger workflows, privilege becomes part of the attack surface. That shifts the governing question from model accuracy to who or what is allowed to act, under which identity, and with what revocation path. Practitioners should treat AI access as a living identity estate, not a static application permission set.
A few things that frame the scale:
- 67% of organisations still rely heavily on static credentials despite the risks they pose to agentic AI deployments, according to the 2026 Infrastructure Identity Survey.
- Only 13% of organisations feel extremely prepared for the reality of agentic AI despite the majority racing toward autonomous adoption, according to the 2026 Infrastructure Identity Survey.
A question worth separating out:
A: Teams should place enforcement at the action layer, not only inside the model. Guardrails can shape text and refuse many bad prompts, but they remain probabilistic and advisory. Runtime governance should check authorization, policy, and audit at the exact moment a tool call is made, so a manipulated agent can be stopped before money moves, data is deleted, or records are changed.
👉 Read our full editorial: AI security risk is outpacing controls for LLMs and agents