Notifications
Clear all
Topic starter
06/06/2026 11:29 am
TL;DR: AI tooling is collapsing knowledge retrieval, delivery timelines, and working skill thresholds into shorter, more accessible workflows, according to WorkOS. That compression raises the leverage of experienced teams, but it also creates hidden lossiness that identity and security programmes must account for.
NHIMG editorial — based on content published by WorkOS: Knowledge compression, time compression, skill compression
Questions worth separating out
Q: How should security teams govern AI-assisted workflows that compress approvals and handoffs?
A: Security teams should identify where AI tooling removes the artefacts that normal governance depends on, such as tickets, peer review, and explicit handoffs.
Q: Why does AI-driven compression create identity governance risk?
A: It creates risk because governance frameworks assume time, evidence, and accountability are visible long enough to review.
Q: What do organisations get wrong about faster AI-powered delivery?
A: They often treat speed as proof that the control model is working.
Practitioner guidance
- Map which approvals disappear under AI-assisted delivery Identify workflows where briefs, review cycles, tickets, or QA gates no longer appear because one person can complete the task in a single session.
- Separate retrieval speed from governance trust Require source provenance, entitlement checks, and review rules for any workflow that compresses knowledge retrieval into a single query path.
- Preserve expert review where compression hides error Mark infrastructure, security, and accessibility tasks that can be drafted quickly but still need specialist sign-off before deployment.
What's in the full article
WorkOS's full article covers the practical examples and product context this post intentionally leaves for the source:
- How the author uses RAG, Claude Code, and MCP servers to illustrate knowledge and workflow compression in practice
- The internal WorkOS example of compressing multi-step operational work into a single continuous session
- The discussion of how compressed workflows can reduce the training path for junior staff and change specialist development
- The author's own view on where judgment should slow the workflow down even when tools make it faster
👉 Read WorkOS's analysis of AI tooling compression and workflow speed →
AI tooling compression: what it means for IAM teams?
Explore further
06/06/2026 12:19 pm
Compression changes the identity problem because governance was built for slower, more legible work. Access review, approval, and sign-off models assume there is enough time to observe activity, compare it against policy, and intervene before impact. AI-assisted compression shortens that window and reduces the artefacts those controls depend on. The practitioner conclusion is that governance has to measure work at the speed it is now being executed, not the speed it used to take.
A few things that frame the scale:
- 98% of companies plan to deploy even more AI agents within the next 12 months, despite documented rogue behaviour in 80% of current deployments, according to AI Agents: The New Attack Surface report.
- That same research found that only 44% of organisations have implemented any policies to govern AI agents, even though 92% say governance is critical to enterprise security.
A question worth separating out:
Q: Should teams use AI compression differently for humans, NHIs, and autonomous systems?
A: Yes. Human users still need training and review, NHIs need scoped access and traceable execution, and autonomous systems need tighter oversight because they can change actions at runtime. The common rule is the same: when compression removes governance artefacts, the control model must become more explicit, not less.
👉 Read our full editorial: AI tooling is compressing knowledge, time, and skill work
