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22 articles from Crelis on NIST AI RMF. Most recent: “An AI Agent Security Policy: What Belongs In One”.
A practical policy separates agent proposals from permission, names approval states, tests material changes, and keeps evidence for disputed actions.
Agent security maps help only if they lead to evidence for the permission behind a consequential action.
NIST's agent standards work matters, but it does not prove that a payment release, deleted record, or changed credit limit was authorised.
An LLM judge can return a different verdict on the same facts tomorrow; a deterministic policy engine returns the same decision each time and can name the rule that made it — that difference decides w
As of January 1, 2026, the legal landscape shifted permanently.
Dynatrace found that nearly half of organizations discard log data, excluding an average of 86% of it. Tamper-evident AI audit logs turn passive monitoring into proof of authorized execution.
ClearPoint found that only 14.7% of AI-related metrics have a named owner. That structural void leaves autonomous agents operating without a definitive chain of command.
Traditional policy frameworks cannot govern non-deterministic agentic systems. Paper-based compliance is dead, and selecting the right AI compliance platform is now a matter of legal survival.
An autonomous AI agent is a high-stakes liability the moment it operates without oversight. The gap between raw model output and enterprise accountability is widening.
McKinsey found that 88% of organizations report regular AI use in at least one business function. Governance has not kept pace, and that gap turns a pilot into a liability.
The era of "move fast and break things" has ended at the regulatory border. Every autonomous decision your system makes is a potential point of failure without a verifiable trail. You know that retros
Autonomous agents can make decisions that a legal team may struggle to defend. Speed is a poor substitute for security. Non-deterministic systems carry inherent risk.
Will your current GRC platform actually stop an autonomous agent from breaching the EU AI Act, or will it simply record the disaster in a tidy PDF? The gap between documentation and enforcement is now
The EU AI Act's high-risk obligations were deferred to 2 December 2027 by the Digital Omnibus. That is preparation time, not a reprieve, and most organizations are not using it.
Grant Thornton found that just 18% of banking leaders were fully confident they could pass an independent review of their AI controls in the next 90 days.
The moment an autonomous agent executes a six-figure transfer without explicit human authorization, the technology ceases to be an asset. It becomes a liability. Most enterprises currently operate in
Theatrical oversight is the greatest hidden liability in your AI stack. Most governance processes are performances: they leave no evidence that a human ever meaningfully engaged.
From 2 December 2027 the EU AI Act's obligations for standalone high-risk systems begin to apply. "Best effort" AI compliance is running out of road.
The gap between an AI's proposal and an enterprise's permission is where catastrophic liability lives. Trust is a systemic vulnerability. You recognize that LLM agents exhibit unpredictable emergent b
Ungoverned AI agents are not assets. They are liabilities. Without a verifiable governance layer, your production models operate in a vacuum of accountability.
The framework a bank needs is not a policy document. It is the ability to show, for one agent action, the authority it relied on, the decision that let it through, and a record of both that does not d
Policy is not proof. In the high-stakes environment of Singapore’s regulatory landscape, a document stating your AI is "safe" holds no weight against a systemic failure. You're facing a reality where