Enterprise AI for CISOs & Security Leaders

AirgapAI operates 100% air-gapped with zero network connectivity to external services. All AI processing happens on your hardware within your network perimeter. No queries, documents, or data ever leave your environment - making data exfiltration through the AI system impossible.

Yes. AirgapAI is SCIF-approved and certified for use in nuclear facilities. It meets the stringent requirements of organizations handling classified, ITAR-controlled, and CUI data. The complete air-gap ensures compliance with the most restrictive security environments.

AirgapAI integrates with your existing identity management (Active Directory, LDAP, etc.) and provides role-based access controls. All user actions are logged with complete audit trails for security monitoring and compliance reporting.

AirgapAI is SCIF-approved, certified for nuclear facilities, and designed for CMMC, ITAR, CUI, HIPAA, and PCI-DSS compliance. Because it operates completely on-premise with no cloud connectivity, it meets data residency requirements for any jurisdiction. Which framework actually governs a given deployment depends on the data and the sector, mapped out on the compliance for AI page.

Yes, most cloud AI providers retain the right to process your data for model improvement, even with enterprise agreements. Cloud providers can also be compelled to provide data access through legal processes. AirgapAI eliminates these risks entirely - your data never leaves your control.

Five controls, in order. First, a data classification that says which tiers AI may touch at all. Second, a deployment rule that maps each tier to an architecture — governed cloud for public and internal work, on-premise or air-gapped inference for confidential and restricted work. Third, an acceptable use policy that names approved tools rather than describing categories, so an employee can check a list instead of interpreting one. Fourth, a third-party review that runs at intake rather than after a pilot has a business sponsor. Fifth, logging and least-privilege scoping for anything that takes actions rather than only answering. The order is what makes it work: a policy written before the classification and the architecture exist is a document the security team cannot audit against.

AI TPRM is third-party risk management extended to systems that consume and generate data rather than only storing it. A standard questionnaire establishes where data rests, who can access it and which attestations apply. An AI review adds five groups on top: data flow and residency, including which sub-processors see prompt content; training use and retention, including whether input trains shared models and whether zero retention is contractually available and verifiable; the model and software supply chain, which extends NIST SP 800-161 practice to model weights, update signing and prompt-injection controls; identity, access and exportable per-prompt audit evidence; and assurance, incident notification and a documented exit path with deletion certification. The groups are additive — they sit alongside the existing review rather than replacing it.

Through a five-step workflow: intake with a named business owner and a declared data category; classification of the highest sensitivity tier the tool will touch in normal use; matching that tier to a deployment mode that can pass, which for confidential and restricted data means on-premise or air-gapped inference; a scored security and third-party review, with a second pass for anything that acts with delegated credentials; and approval with conditions — allowed data tiers, access scope, logging destination, human-review rules and a review date — recorded by name in the approved-tools register. Publishing denials matters as much as publishing approvals, because an unanswered request becomes shadow AI within weeks.

Security controls answer "can this system be trusted with this data"; governance answers "who decided, on what evidence, and when is that decision revisited". For a CISO that means four additions to the control set: a named accountable owner per approved system, so risk acceptance has a signature; a mapping from the organization's AI risks to a recognised structure such as the NIST AI Risk Management Framework or ISO/IEC 42001, so an auditor sees a method rather than a list; documented human-review and disclosure rules for AI-assisted output; and a review cadence, because the approved-tools list goes stale within a quarter as products ship new model versions and new agentic features. Governance is also where security stops being the only voice — legal, privacy, and the data owners each hold part of the decision.

Evaluate agents on permissions and evidence rather than on model quality. The controls that matter are least-privilege credentials scoped per tool and per data source rather than a single broad service account; a human approval gate on write, spend and external-communication actions; full traceability from a triggering prompt through every tool call to the resulting action, exported to the SIEM in a standard format; defences against prompt injection and tool poisoning in retrieval and tool-use paths; and a kill switch that revokes an agent's credentials without redeploying it. Deployment location remains the outer control: an agent operating on confidential or restricted data should run where that data already lives. The AI agent security checklist on this site sets out the OWASP, NIST and CISA-aligned controls in full.