CCTIData Security and LLM Governance in the Enterprise
Deploying large language models without turning proprietary data into a compliance risk.
The real blocker is data, not capability
Most organizations can already get value from language models. What stops them is the question of where their data goes: source code, contracts, technical drawings and customer records sent to an external API leave the organization's control.
Governance as architecture, not only policy
Data boundaries. Sensitive workloads run on infrastructure the organization controls — on-premise or a private cloud tenant — so that confidential material does not leave the perimeter and is not used to train third-party models.
Control of the flow. Inputs from documents, web pages and tools are treated as data, never as instructions; this is the main defence against indirect prompt injection, now the most common attack on AI agents.
Accountability. Every model output that feeds a decision is logged with its sources, so that a human can verify it and remains responsible for the decision.
Alignment with emerging frameworks
These controls map onto the risk-management practices of the NIST AI Risk Management Framework and the obligations introduced by the EU AI Act. No architecture removes risk entirely; the goal is risk that is known, bounded and auditable.