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01/08/2026 8:58 pm
TL;DR: AI agents now connect to tools, MCP servers, credentials, files, and workflows, so security teams need discovery, adversarial testing, and runtime enforcement rather than model scanning alone, according to Straiker's 2026 Agentic Threat Report. The practical shift is from visibility-only AI-SPM toward controls that govern what agents can actually do in production.
NHIMG editorial — based on content published by Straikerai: Top 7 Agentic AI Security Platforms for 2026
Questions worth separating out
Q: What breaks when AI agents are treated like standard human users?
A: You lose visibility into effective permissions, expected behaviour, and real blast radius.
Q: Why do AI agents complicate existing IAM and PAM controls?
A: AI agents complicate IAM and PAM because they often inherit delegated credentials, operate across multiple systems, and keep acting after the initial approval moment has passed.
Q: How can security teams tell whether agent access is actually under control?
A: Look for evidence that the team can trace every tool call, secret use, and cross-system action back to a named owner and a valid approval path.
Practitioner guidance
- Map every agent to a named owner and policy boundary Assign each production agent to a business owner, technical owner, and explicit policy scope.
- Inventory MCP connections as part of access governance Treat each MCP server, connector, and tool integration as a privileged access path.
- Test agent workflows for multi-step abuse paths Use adversarial testing to see whether a malicious prompt, poisoned document, or indirect instruction can move an agent from data retrieval to unauthorized tool use.
What's in the full article
Straiker's full report covers the operational detail this post intentionally leaves for the source:
- Platform-by-platform feature breakdown across discovery, red teaming, runtime protection, and MCP governance.
- Buyer-oriented guidance on how to compare agentic AI security stacks against real production workflows.
- Category-specific evaluation notes for chatbot guardrails, AI red teaming, and agentic SOC use cases.
- The report's own framing of where each platform sits in the broader 2026 market.
👉 Read Straiker's 2026 guide to the top 7 agentic AI security platforms →
AI agent security platforms in 2026: what do teams still miss?
Explore further
01/08/2026 9:05 pm
AI agent security is becoming an identity governance problem before it becomes a model safety problem. The article's real contribution is to show that agents need to be governed as acting identities with tools, credentials, and runtime authority. That shifts the control conversation away from prompts and toward authorisation boundaries, delegation, and auditability. Practitioners should read this as a call to define what an agent is allowed to do, not just what it is allowed to say.
A question worth separating out:
Q: Should organisations prioritise MCP governance before expanding agent deployments?
A: Yes, when agents rely on external tools and data sources, MCP governance should be in place before broad rollout. The reason is simple: each connection is a new trust path, and unreviewed connectors can expand blast radius faster than security teams can respond. Inventory and policy are prerequisites, not afterthoughts.
👉 Read our full editorial: AI agent security platforms in 2026 still need runtime control
