Loading…
Loading…
20 articles from Crelis on NIST AI RMF. Most recent: “NIST's AI Agent Standards Work: What Is In Scope”.
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
Your autonomous agents are making decisions your legal team cannot defend. Speed is a poor substitute for security. You recognize the inherent danger in non-deterministic systems. A single unauthorize
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.
The EU AI Act became generally applicable on 2 August 2026, with high-risk obligations following on 2 December 2027. "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