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02/08/2026 12:21 pm
TL;DR: AI security vulnerabilities now span prompt injection, data leakage, model poisoning, tool misuse, and shadow AI as autonomous agents connect to APIs, memory, and external systems, according to Akto. Static controls cannot fully govern probabilistic systems that act at runtime, so discovery, least privilege, and continuous testing become the decisive controls.
NHIMG editorial — based on content published by Akto: AI Security Vulnerabilities: Understanding Risks, Threats and Mitigations
Questions worth separating out
Q: How should security teams govern AI agents that call APIs instead of using a UI?
A: Security teams should govern AI agents by treating each callable action as a scoped entitlement, not as a general application login.
Q: Why do AI agents complicate traditional access reviews?
A: AI agents complicate access reviews because they can accumulate permissions across tools and environments faster than manual certification cycles can observe.
Q: What breaks when prompt injection is not controlled in agentic workflows?
A: When prompt injection is not controlled, the agent can follow malicious instructions hidden in web content and perform actions the user never intended.
Practitioner guidance
- Inventory every AI system and connected tool Build a live register of models, agents, browser extensions, APIs, and MCP-connected tools, including shadow AI discovered on employee devices and cloud workloads.
- Scope each agent to task-level permissions Separate read and write access, block unnecessary tool calls, and remove broad API scopes that let a manipulated prompt reach unrelated systems.
- Monitor runtime actions, not only prompts Capture which tools an agent invoked, what data it touched, and which state changes it attempted.
What's in the full article
Akto's full blog covers the operational detail this post intentionally leaves for the source:
- Step-by-step mitigation guidance for prompt injection, model poisoning, and tool misuse in production AI systems
- A practical checklist for inventorying AI agents, APIs, plugins, and shadow AI across environments
- Operational advice for protecting vector stores, context windows, and third-party AI dependencies
- Examples of runtime guardrails and red teaming approaches for agentic workflows
👉 Read Akto's analysis of AI security vulnerabilities, agentic risk, and mitigation →
AI agent attack surfaces are changing fast, are your controls keeping up?
Explore further
02/08/2026 1:21 pm
AI security is becoming identity security by another name. Once models and agents can act through APIs, browsers, and tools, they behave like non-human identities with scoped privileges and measurable blast radius. The article correctly shows that the main control problem is no longer just model safety, but permissioned access to operational systems. That makes IAM, PAM, and NHI governance central to AI security rather than adjacent to it. Practitioners should govern agents as identities, not abstractions.
A question worth separating out:
Q: Who is accountable when an AI agent makes an unauthorised change?
A: Accountability should be assigned to the governance model that authorised the delegation, the owner of the workflow, and the team that set the policy boundary. In practice, organisations need clear responsibility for agent configuration, monitoring, and incident response because the machine’s speed does not remove human accountability for the delegated identity.
👉 Read our full editorial: AI security vulnerabilities expose the gap between static controls and agents
