Notifications
Clear all
Topic starter
02/08/2026 11:32 am
TL;DR: AI-generated code, prompt injection, shadow AI, and unsafe agent execution are now core enterprise risks, with Aikido’s 2026 State of AI in Security report finding one in five organisations has already suffered a serious incident tied to AI-generated code. The governance gap is widening faster than legacy scanning and access controls can absorb it.
NHIMG editorial — based on content published by Akto: 15 Best AI Security Tools in 2026 to Secure AI Agents, GenAI and LLMs
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.
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 governance programmes need both policy and runtime controls?
A: Policy defines intent, but runtime controls decide whether that intent is enforced where AI actually operates.
Q: What do security teams get wrong about Shadow AI?
A: They often treat Shadow AI as an approval problem for software, when it is usually also an identity problem.
Practitioner guidance
- Inventory AI agents, MCP workflows, and connected tools Map every model, agent, plugin, API connection, and browser or SaaS integration that can act on enterprise data.
- Separate read-only prompts from action-capable tool calls Design distinct controls for inference and execution.
- Apply least privilege to AI-connected credentials and tokens Treat AI service accounts, API keys, and delegated tokens as non-human identities with defined owners, expiries, and revocation paths.
What's in the full article
Akto's full article covers the operational detail this post intentionally leaves for the source:
- Side-by-side vendor evaluation criteria for AI security tools across runtime, red teaming, posture, and governance layers
- Tool-specific comparisons for agent discovery, MCP security, and LLM red teaming that are useful once you have already defined your control model
- Implementation-oriented considerations such as CI/CD integration, deployment flexibility, role-based access controls, and audit logging
- Product-by-product limitations that help teams decide where AI security tooling should sit in a broader control stack
👉 Read Akto's comparison of AI security tools for agents, LLMs, and MCP systems →
AI security tools are now an IAM and governance problem?
Explore further
02/08/2026 12:47 pm
AI security has become an identity governance problem as much as a detection problem. Once an AI system can call tools, access data, or act on behalf of a user, it behaves like a governed non-human identity rather than a simple application. That shifts the control question from whether the model is accurate to whether its privileges are bounded, reviewable, and revocable. Practitioners should treat AI agents as access-bearing systems that need lifecycle governance.
A question worth separating out:
Q: How can organisations tell whether AI governance is actually working?
A: Organisations can tell AI governance is working when they can inventory every agent, explain its purpose, show who owns it, and prove that permissions are tightly scoped. If those four things are missing, the programme has policy language but not operational control. Auditors will notice the gap quickly.
👉 Read our full editorial: AI security tools are now an IAM and governance problem
