Topic starter
02/08/2026 11:24 am
TL;DR: Generative AI security risks now span prompt injection, data leakage, excessive agent permissions, MCP server exposure, and runtime manipulation as LLMs and AI agents reach production systems, according to Akto. Static policies are not enough when agents can act independently; discovery, guardrails, runtime protection, and continuous testing now define workable control coverage.
Editorial analysis by NHI Mgmt Group, based on content published by Akto: “Generative AI Security Risks: Threats, Attacks, and Defenses”.
Key questions
Q: What fails when GenAI systems rely on static policy instead of runtime enforcement?
A: Static policy fails because it describes intended behaviour, not live behaviour.
Q: Why do over-privileged AI agents create higher business risk than ordinary users?
A: Over-privileged agents can execute many more actions than the task actually requires, so one bad decision can cascade into wrong approvals, refunds, data movement, or API calls.
Q: What are the signs that GenAI use is becoming unsafe in an organisation?
A: Warning signs include employees sharing sensitive information with GenAI tools, teams relying on model output without review, and inconsistent answers being used in business processes.
Practitioner guidance
- Inventory every AI agent and connected tool Track each model, agent, MCP server, and API integration so security teams know what exists, what it can reach, and which systems it can influence.
- Scope agent authority to the task Assign the minimum access needed for the workflow and remove standing permissions that let an agent touch unrelated data or systems.
- Deploy runtime guardrails on tool use Block risky tool calls, sensitive data disclosure, and out-of-policy actions at execution time rather than relying on pre-approved prompts or static rules.
Bottom line: GenAI risk is no longer confined to prompt quality or model output. It now spans the full path from instruction handling to tool execution and runtime behaviour.
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02/08/2026 12:41 pm
GenAI security is now an identity governance problem disguised as an application security problem. Once agents can call tools, access data, and make decisions at runtime, the real control question becomes who or what is authorised to act, under which conditions, and with what blast radius. That is an NHI problem as much as an AI problem, because the agent behaves like a non-human identity with delegated authority. Practitioners should govern the agent's runtime identity, not just the model configuration.
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.
- Half of companies using generative AI will deploy agentic AI by 2027, according to Deloitte's 2025 Technology Predictions.
A question worth separating out:
Q: Which controls matter most when governing autonomous AI systems?
A: The most important controls are discovery, least privilege, runtime guardrails, audit logging and a clear decision owner for access changes. Together they reduce the chance that an agent can act outside approved scope and they give security teams evidence when an incident occurs.
👉 Read our full editorial: GenAI security risks now span prompts, tools, and runtime behavior