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02/08/2026 11:54 am
TL;DR: AI systems now face prompt injection, data poisoning, leakage, and autonomous tool abuse, and Akto’s guide argues that traditional IT risk methods are too static for agentic environments. The real shift is that AI governance now depends on continuous monitoring, adversarial testing, and identity-aware access control, not just pre-launch review.
NHIMG editorial — based on content published by Akto: AI Risk Management Framework: Components, Risks and Implementation Guide
By the numbers:
- When AWS credentials are exposed publicly, attackers attempt access within an average of 17 minutes and as quickly as 9 minutes in some cases.
- NHIs outnumber human identities by 25x to 50x in modern enterprises.
- Only 5.7% of organisations have full visibility into their service accounts.
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 create a different access-risk profile than traditional applications?
A: AI agents can chain actions, call multiple tools, and change behaviour based on context, so one credential can enable more than one operational path.
Q: What do teams get wrong about AI guardrails and identity controls?
A: They often assume a content filter is a substitute for access governance.
Practitioner guidance
- Define an AI identity inventory List every LLM, agent, RAG pipeline, and AI-powered workflow, then record the data sources, tools, and credentials each one can reach.
- Separate inference from execution Require policy checks before an agent can call APIs, write data, or trigger business actions.
- Instrument identity-rich telemetry Log the agent principal, tool calls, data sources, policy decisions, and downstream actions so investigations can reconstruct the full decision chain.
What's in the full article
Akto's full blog covers the operational detail this post intentionally leaves for the source:
- Step-by-step AI inventory and classification workflow for LLMs, RAG pipelines, and agentic features.
- Practical examples of prompt injection testing, red teaming, and guardrail placement across AI workflows.
- Monitoring and audit-readiness guidance for tracking inputs, outputs, policy violations, and abnormal model behaviour.
- Framework comparisons across NIST AI RMF, ISO/IEC 42001, and OECD AI Principles for programme owners.
👉 Read Akto's guide to AI risk management frameworks for agentic systems →
AI risk management for agents: are your controls keeping up?
Explore further
02/08/2026 1:03 pm
AI risk management has become an identity governance problem as much as a model risk problem. Once an AI system can authenticate to services, call tools, and take actions, it behaves like a non-human identity with its own lifecycle and privilege profile. That is why conventional application testing is insufficient on its own. IAM and PAM teams need to treat agents as governed actors, not just software features. The practical conclusion is that AI security programmes must inherit identity controls, not sit beside them.
A question worth separating out:
Q: Who is accountable when an AI system moves data outside policy?
A: Accountability should sit with the team that owns the AI workflow, the data it touches, and the credentials that enable it. If governance stops at authentication, ownership becomes blurred. Clear accountability means mapping the data path, the action scope, and the approving function before deployment.
👉 Read our full editorial: AI risk management now needs identity-aware controls for agentic systems
