Enterprise adoption of artificial intelligence is moving far faster than the governance structures meant to keep it in check, exposing organisations to risks that traditional oversight tools were never built to catch.
According to Theta Lake, the widening gap between what AI systems are capable of and what businesses can realistically monitor is becoming a central concern for risk and compliance teams.
Ryan Greenblatt, who led the transcript analysis into the OpenAI/Hugging Face hacking incident, said, “The difficulty of understanding incidents and overseeing AI agents appears to be growing faster than the rate at which more capable AIs help us with oversight and understanding.”
Conventional frameworks such as SOC 2 and ISO 27001 were not designed with AI-specific risks in mind. They offer no defence against prompt injection, no way to track model drift or hallucination, and no mechanism for capturing human-to-AI interactions, leaving organisations without a route to certifiable compliance under frameworks including ISO/IEC 42001, the EU AI Act, and the NIST AI Risk Management Framework.
A genuinely complete AI compliance solution needs to address three core pillars. The first is discovery: identifying every AI system in use across an organisation, including unauthorised “Shadow AI” adopted by employees outside IT’s knowledge.
The second is data and model governance, covering training data provenance, prompt-layer data loss prevention, and explainability documentation for regulators. The third, and most frequently neglected, is communications and interaction governance, which captures the actual prompts, responses and conversations exchanged between people and AI tools daily.
This interaction layer, often called Digital Communications Governance and Archiving (DCGA), is where many compliance programmes fall short. Without normalised review processes, intelligent incident routing to the right stakeholder teams, and continuous forensic re-scanning of historical data, organisations risk missing subtle, cumulative patterns of misuse that only become visible in hindsight.
For procurement and risk teams evaluating vendors, a four-point checklist is proposed: automated Shadow AI discovery, ISO/IEC 42001 certification of the vendor’s own platform, full-context investigation views of AI interactions, and enforced DLP at the prompt layer. Gaps in any of these areas, the guide argues, represent real compliance exposure rather than theoretical risk.
Read the full Theta Lake post here.
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