Defending AI Governance with Auditable Trails and Model Locking

Published a new working paper on AI governance today. Can you show that a control actually operated for a given AI system during a given period? That's a hard question to answer quickly. Auditors want evidence the control actually ran. In this paper I propose Control Intelligence: an architectural layer that treats controls as the core unit of compliance, connecting regulatory requirements, applicability decisions, controls, execution records, and evidence into a single traceable model. The paper also introduces Governance Friction, a metric for the operational cost of keeping a human in the loop. Working paper (DOI: 10.5281/zenodo.21625573): PDF attached.

Active Intelligence™ iXBRL Tagging combines advanced AI with expert human oversight to automate the tagging for Tailored Shareholder Reports and N-CSR filings. This allows teams to move faster, reduce manual effort, improve consistency, and reduce customer cycle times, while maintaining the control required in highly regulated reporting environments. Learn more: https://lnkd.in/epKwh6pu

Active Intelligence™ iXBRL Tagging combines advanced AI with expert human oversight to automate the tagging for Tailored Shareholder Reports and N-CSR filings. This allows teams to move faster, reduce manual effort, improve consistency, and reduce customer cycle times, while maintaining the control required in highly regulated reporting environments. Learn more: https://lnkd.in/gTgANviV

Everyone heard that the EU just postponed the AI Act. Almost nobody read what it kept in place. The Digital Omnibus — endorsed by the European Parliament on June 16 and by the Council on June 29 — moves the high-risk obligations: December 2, 2027 for stand-alone systems (Annex III), August 2, 2028 for AI embedded in regulated products. What did NOT move: Article 50. From August 2, 2026 — five days from now — transparency becomes law: 1. People must be told when they are interacting with an AI system. Your chatbot has to introduce itself. 2. AI-generated and AI-manipulated content must be identifiable as such. Deepfakes must be disclosed. 3. Machine-readable watermarking of synthetic content — with one concession: systems already on the market before August 2 get a grace period until December 2, 2026. So the real picture is not "Brussels blinked". It is a swap: more time on the hardest requirements, zero time on the most visible ones. Here is the trap I see in boardrooms: treating 16 extra months as 16 months of not thinking about it. The companies that will clear December 2027 without drama are the ones using this window to do the unglamorous work — inventory of AI systems in use, risk classification, guardrails built into workflows — while their competitors celebrate the deferral. The deadline moved. The direction didn't. Is your roadmap treating the deferral as breathing room — or as an excuse? #AIAct #AIGovernance #Compliance

Generative AI has made "just build it ourselves" tempting for financial institutions rethinking communications surveillance. Firms can have more control over the model, in theory. But factor in cost escalation, regulatory accountability, cross-border data governance, and key-person risk — and building starts to look a lot more expensive than it did on paper. Our new white paper breaks down 5 risks firms should weigh before building proprietary AI surveillance in-house — and where purpose-built platforms close the gap ⬇️ 👉 Get the white paper: https://lnkd.in/em6hKj58

Buy vs. build decisions have always centered on "build to order" based upon available technology at a specific point-in-time versus designing a system for resiliency, scale, and cost effectiveness. That analysis may have never been more important than in examining investments in #LLMs. Read our take in this most recent paper.

What if your compliance team could reach a baseline score in hours, not weeks? NISTcompliance․ai brings together secure, Government-hosted AI, shared evidence and automated workflows to simplify RMF and FISMA compliance. Access the Tech Spotlight here: https://ow.ly/gElu30sXrcS

Building a trust fabric in health data starts with integrated AI governance. The release of DiME’s framework emphasizes that AI governance must be embedded into operations, not treated as an abstract ethical aspiration. It defines concrete components such as role delineation, risk‑tiering, model lifecycle management, monitoring and oversight, and cross‑functional governance committees.https://https://lnkd.in/ghf-2szf

Insurers racing to embed AI into their underwriting and claims services may be creating fresh compliance risks, according to experts. https://lnkd.in/eFt_Mg57 #riskmanagement M-Files

Insurers racing to embed AI into their underwriting and claims services may be creating fresh compliance risks, according to experts. https://lnkd.in/ecytXBTh #riskmanagement M-Files