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25/06/2026 10:32 pm
TL;DR: Anthropic’s Mythos pushed attack execution into machine time, collapsing detection, review, and remediation windows that IAM, PAM, and IGA have depended on for three decades, according to Silverfort. Access review processes assume privilege persists long enough to be observed, but autonomous execution can create and discard it inside a single session.
NHIMG editorial — based on content published by Silverfort: Why Silverfort's Mythos analysis is changing identity security thinking
By the numbers:
- 38 independent researchers reported findings that went well beyond benchmarks after Claude Mythos Preview was released.
- Only 5.7% of organisations have full visibility into their service accounts.
Questions worth separating out
Q: How should security teams govern AI agent security in runtime environments?
A: Security teams should govern AI agent security at the point of access, not only through provisioning and periodic review.
Q: Why do AI agents complicate traditional IAM and PAM controls?
A: AI agents complicate traditional IAM and PAM controls because they compress identity decisions into short, non-linear execution chains.
Q: What breaks when access review cycles are used for machine identities?
A: What breaks is the assumption that access will still exist long enough to be reviewed.
Practitioner guidance
- Shift enforcement into the access path Evaluate authentication and authorisation requests at runtime using live context, not after the fact in review queues.
- Baseline every AI agent identity Assign each agent a known owner, explicit purpose, and scoped access boundary before it is allowed to act.
- Reduce standing privilege for service accounts Inventory service accounts and secrets that can reach high-value systems without a justifiable task boundary.
What's in the full article
Silverfort's full blog post covers the operational detail this post intentionally leaves for the source:
- The runtime access protection workflow that evaluates identity context before authentication completes
- The AI agent security controls Silverfort describes for inline enforcement across human, machine, and agent identities
- The access decision loop examples showing how permissive, constrained, and redirected responses differ in practice
- The product framing for legacy infrastructure coverage and how the vendor positions AI agent protection inside that model
👉 Read Silverfort's analysis of AI agent security and runtime identity control →
AI agent security at runtime: are your identity controls keeping up?
Explore further
25/06/2026 11:06 pm
Runtime governance is now the control plane, not an enhancement. The article’s central insight is that execution has moved into a window too short for traditional IAM governance to observe. Access reviews, periodic certification, and delayed detection were designed for identities whose privilege persisted long enough to be reviewed. That assumption no longer holds when AI agents and machine-speed attacks can create, use, and discard access within one continuous chain. Practitioners should treat runtime enforcement as the primary identity security boundary.
A few things that frame the scale:
- 91.6% of secrets remain valid five days after the targeted organisation is notified, showing a critical gap in remediation procedures, according to the Ultimate Guide to NHIs.
- 96% of organisations store secrets outside of secrets managers in vulnerable locations including code, config files, and CI/CD tools, according to the Ultimate Guide to NHIs.
A question worth separating out:
Q: Who should own revocation for AI agent and service account access?
A: Ownership should sit with the team that can revoke access in time and understand the operational purpose of the identity. If no one can act before the chain completes, accountability is only theoretical and the control model is already too slow.
👉 Read our full editorial: Runtime identity controls for AI agent security are now mandatory
