Topic starter
06/09/2026 6:20 pm
TL;DR: Akeyless says AI assistants and autonomous agents that reach systems outside SSO are prompting users to paste passwords, API keys, and tokens into conversations, reviving the secrets management problem in business workflows. The core issue is not human authentication but removing credentials from AI access paths before runtime governance becomes the only remaining control.
Editorial analysis by NHI Mgmt Group, based on content published by Akeyless: “M2M vs. User SSO: Why SSO Stops Working When AI Starts Doing the Work”.
By the numbers:
- GitGuardian’s 2026 State of Secrets Sprawl found an 81% year-over-year increase in leaked AI service credentials.
Key questions
Q: What should teams do first when AI tools need access to systems outside SSO?
A: The first move is to remove credentials from the workflow entirely.
Q: Why do AI assistants create a secrets-management problem even when SSO is in place?
A: SSO only covers applications that trust the identity provider.
Q: What breaks when developers paste API keys into AI-built apps?
A: The application loses clean attribution, revocation, and scope control.
Practitioner guidance
- Implement secretless AI access for non-SSO systems Route AI assistants and agents through brokered access paths that authenticate the user, apply policy, and issue only task-scoped access without exposing passwords, API keys, or tokens.
- Remove credential entry from AI workflows Eliminate prompts, forms, and local files that ask employees to paste secrets into AI conversations, especially where the target system is a legacy database, SaaS app, or internal API.
- Classify AI-connected systems as machine-to-machine access paths Treat any AI workflow that calls enterprise systems outside SSO as an NHI governance problem, with ownership, inventory, and lifecycle controls attached to the underlying credentials.
Bottom line: AI assistants that operate outside SSO are reviving the secrets problem by encouraging users to paste credentials into prompts.
What's in the full article
Akeyless's full article covers the operational detail this post intentionally leaves for the source:
- How SecretlessAI brokers access to enterprise systems without exposing passwords, API keys, or tokens to the AI
- How Runtime Authority applies intent-aware policy and task-scoped control once agents begin executing actions
- How the same platform model extends from developer secrets into employee AI workflows across SaaS, APIs, and infrastructure
- How the article frames secretless access and runtime governance as separate stages of AI identity maturity
👉 Read Akeyless's analysis of AI agent access outside SSO and secretless access →
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06/09/2026 6:21 pm
AI access outside SSO is reviving the secrets problem rather than replacing it. The security issue is not that SSO has failed, but that AI workflows often reach systems where SSO never applied in the first place. That creates a new prompt-mediated secret handling path that looks operationally convenient but is governance-light. Identity programmes should treat this as an expansion of the secrets estate, not a new authentication model.
A few things that frame the scale:
- The average estimated time to remediate a leaked secret is 27 days, despite 75% of organisations expressing strong confidence in their secrets management capabilities, according to the State of Secrets in AppSec.
A question worth separating out:
A: They should require runtime controls as soon as the AI can act, not just respond. Once an agent has tools, memory, credentials, and time, policy has to become executable through constraints on scope, permissions, approvals, and monitoring. Model alignment remains useful, but it cannot substitute for controls that enforce boundaries while the system is operating.
👉 Read our full editorial: AI agent access outside SSO is reviving the secrets problem