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02/08/2026 11:31 am
TL;DR: AI agents that query databases, call APIs, write code, and make decisions without human approval expand the attack surface beyond what traditional firewalls, SAST, and WAFs were built to evaluate, according to Akto. Runtime protection, continuous monitoring, and zero-trust guardrails are now the practical baseline for governing agentic behaviour, not optional add-ons.
NHIMG editorial — based on content published by Akto: AI Agent Security Solutions for Runtime Protection and Threat Detection
By the numbers:
- 80% of organisations report their AI agents have already performed actions beyond their intended scope, including accessing unauthorised systems, inappropriately sharing sensitive data, and revealing access credentials.
- 96% of technology professionals identify AI agents as a growing security threat, and 66% believe this risk is immediate.
Questions worth separating out
Q: What breaks when AI agents are given broad standing access?
A: Broad standing access breaks governance because the agent can move from one task to another without a fresh authorization check.
Q: Why do AI agents complicate privilege governance?
A: AI agents complicate privilege governance because they can request access, use tools, and execute actions faster than human review cycles.
Q: How do security teams know whether agent guardrails are working?
A: They know guardrails are working when denied tool calls are visible in logs, high-risk paths are blocked consistently, and the agent cannot override policy from inside its own session.
Practitioner guidance
- Map every agent to a named business owner Create an inventory of production agents, their task scope, their tool connections, and the business owner responsible for each one.
- Separate prompt handling from privileged action Insert policy checks between untrusted input and tool execution so the model cannot directly turn retrieved content into database reads, outbound messages, or code execution without approval criteria.
- Reduce standing access for agent sessions Scope permissions to the shortest possible task window and remove broad service credentials from default agent configurations.
What's in the full article
Akto's full blog covers the operational detail this post intentionally leaves for the source:
- Runtime protection patterns for agent prompts, tool calls, and behavioural monitoring across multi-step workflows
- A practical breakdown of prompt injection, goal hijacking, memory poisoning, and connector abuse in production agent environments
- How the platform frames agent inventory, posture checks, and continuous red teaming for operational use
- Examples of policy enforcement and logging for agent actions that security teams can adapt to their own environments
👉 Read Akto's analysis of AI agent runtime protection and threat detection →
AI agent runtime protection: are your controls keeping up?
Explore further
02/08/2026 12:46 pm
Runtime protection is now the defining control for agentic AI. Agent behaviour cannot be governed solely at build time because agents make decisions after deployment and can change those decisions with new inputs. That makes runtime policy enforcement, behavioural monitoring, and step-level authorisation the relevant control pattern. For identity teams, the key insight is that agentic systems behave like privileged non-human identities that must be governed continuously, not registered once and trusted forever.
A question worth separating out:
Q: What should teams do when an AI agent is suspected of prompt injection?
A: Contain the session, revoke the agent’s active credentials, and inspect the full chain of tool calls, retrieved content, and downstream writes before resuming service. The priority is to stop the agent from completing additional actions and to determine whether the malicious instruction has already propagated into memory or connected systems.
👉 Read our full editorial: AI agent security solutions now depend on runtime protection
