Topic starter
02/08/2026 12:09 pm
TL;DR: AI agents, prompt injection, tool misuse, memory poisoning, and identity abuse are converging into a broader AI security problem as enterprises deploy autonomous workflows with internal access, according to Akto. The old application-security model breaks because these systems make decisions at runtime and can cascade a single compromised step across enterprise tools.
Editorial analysis by NHI Mgmt Group, based on content published by Akto: “AI Security Solutions: Securing LLMs, AI Agents, and Enterprise AI in 2026”.
Key questions
Q: What breaks when AI agents are given broad enterprise access without tight governance?
A: Broad access turns AI agents into high-speed execution paths that can move data, spend money, modify records, or delete assets before operators can intervene.
Q: Why do autonomous agents create more security risk than standard LLM applications?
A: Autonomous agents create more risk because they can act, not just generate text.
Q: What are the signs that an AI agent is failing or drifting outside its intended mission?
A: Common warning signs include goal drift, repeated no-progress calls, unusual tool use, privilege escalation attempts, credential misuse, and unexpected agent-to-agent communication.
Practitioner guidance
- Define runtime guardrails for agent actions Place policy checks between the model decision and the external tool call so that sensitive actions can be blocked or downgraded before execution.
- Inventory all shadow AI and agentic workflows Build a living inventory of models, copilots, agent endpoints, tool connections, and data paths so undeclared systems cannot bypass governance.
- Constrain tool permissions by task context Bind each agent to the minimum tool set needed for the task, and separate read, write, delete, and transfer actions wherever possible.
Bottom line: AI agents and LLM apps create runtime risk because they can be steered after access is granted, not just before deployment.
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02/08/2026 1:13 pm
Runtime governance is now the control plane for AI security: The article correctly treats LLMs and agents as systems whose risk emerges at execution time, not just at build time. That changes the centre of gravity for identity and security programmes, because permissions, tool access, and monitoring all have to hold while the system is acting. The practical conclusion is that runtime visibility is no longer optional for AI workloads.
A few things that frame the scale:
- Gartner predicts that AI systems will initiate 50% of all service requests by 2030, driven largely by agentic AI.
A question 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. The control set should include inventory, task-bound credentials, audit trails, and revocation paths. If an agent can call tools or touch production systems, it belongs in the same governance model as service accounts and other machine identities.
👉 Read our full editorial: AI security solutions for LLMs and agents need runtime governance