Topic starter
09/07/2026 3:21 pm
TL;DR: Gartner recognised an AI software security approach while the company framed SAIL 2.0 around discovery, red teaming, and runtime guardrails for AI agents, according to Pillar Security. The broader signal is that AI agent security is moving from point controls to continuous governance over identities, tools, and execution paths.
Editorial analysis by NHI Mgmt Group, based on content published by Pillar Security: “Pillar Security Named as a 2026 Gartner® Cool Vendor in AI Software Security”.
By the numbers:
- Gartner forecasts worldwide AI cybersecurity spending to nearly double in 2026, from $25.9 billion to $51.3 billion, and to reach $86 billion by 2027.
- Just 14% of software engineering teams surveyed have advanced skills in AI security or platform engineering.
Key questions
Q: What breaks when AI agents have production access without containment?
A: What breaks is the assumption that identity permission alone is enough to control impact.
Q: Why do AI agents create more risk than traditional automation?
A: AI agents create more risk because they can interpret context, choose actions, and invoke tools autonomously.
Q: How do security teams know whether an AI agent control stack is actually working?
A: Look for three things: every agent has a traceable identity, permissions are narrow enough to explain in operational terms, and actions can be audited end to end.
Practitioner guidance
- Define agent authority boundaries Document which credentials, tools, and production systems each AI agent may access, and make that boundary explicit before deployment.
- Build a live AI asset inventory Maintain an AI-BOM that links agents, models, prompts, MCP tools, datasets, and configurations to owners and access scope.
- Tie red-team findings to guardrails Convert validated agent attack paths into runtime policies so the next execution attempt is blocked by enforcement, not just documented in a report.
Bottom line: AI agent security is shifting from static approval to runtime governance because agents can combine credentials, tools, and execution paths inside a single session.
Explore further
View Full Forum → | NHI Foundation Course → | Our Services → | Read the full analysis →
09/07/2026 3:39 pm
Runtime governance is becoming the real control plane for AI agents. Once agents can request credentials, call tools, and act inside production systems, static policy checks stop being enough. The control question shifts to what the agent can do at the moment of execution and whether the organisation can constrain it before action completes. For IAM and PAM teams, that means agent governance now belongs in the same conversation as privilege, authorization, and session control.
A few things that frame the scale:
- 19% of organisations give AI systems dramatically more access than human employees, nearly one in five granting unrestricted privilege, according to the 2026 Infrastructure Identity Survey.
A question worth separating out:
Q: What is the difference between AI discovery and AI runtime protection?
A: AI discovery identifies where AI assets exist, such as models, datasets, agents, API calls, and MCP servers, while runtime protection enforces policy when those assets are being used. Discovery gives inventory and context. Runtime protection reduces exposure in active sessions. Mature programmes need both, because visibility without enforcement leaves risk, and enforcement without discovery leaves blind spots.
👉 Read our full editorial: SAIL 2.0 signals a shift toward runtime AI agent security