UK: Concentric AI presses for context-first controls to tame GenAI data risk

Generative AI’s move from experimentation to widespread enterprise use is revealing faults in how organizations discover, classify and govern sensitive data. Concentric AI argues this is not merely a perimeter problem like BYOD, but a structural shift: AI creates new, often invisible channels through which information can flow to third-party models, increasing the chance of inadvertent exposure. The vendor advocates a context-first strategy that begins with semantic-aware discovery across cloud and on-premises stores to locate sensitive assets and understand their business purpose. That classification must then be actionable at runtime: Concentric recommends enforcing controls at the application layer to block or mediate unsafe transfers to external AI services rather than relying solely on network or storage protections. Complementing those controls, the company calls for model-level governance — tracking which models touch data, how they are trained or updated, and maintaining audit trails that link policy to model provenance. Operationalizing the approach requires tooling that couples classification to real-time enforcement, richer telemetry for incident workflows, and a governance posture that treats models as first-class, auditable assets. Those prescriptions align with broader industry trends toward treating machine outputs as untrusted until validated, hardening ingestion and verification pipelines, and favoring architectures that reduce duplicated copies of data to simplify lineage and provenance. Recent reports and incidents showing exposed chat logs, leaked keys and misbehaving agents underscore the need for runtime observability and admission controls that can stop risky behavior before it propagates. Regulatory fragmentation and procurement pressures are also raising the bar: buyers increasingly expect provenance artifacts, auditable logging and contractual remedies that make it safe to run AI in regulated contexts. Concentric plans to present these ideas at a London industry event, aiming to give security teams and buyers concrete steps for merging discovery, enforcement and model governance into repeatable programs. For enterprises, the implication is clear: without context-aware controls and model-level oversight, GenAI projects face greater risk of accidental data leakage, compliance failures and loss of control as AI use grows. Vendors and security teams that can integrate classification, runtime controls and provenance into procurement and operational workflows will be better positioned to enable safe, scalable AI deployments.