Should enterprise buyers require attestations before using AI in production?

Why This Matters for Security Teams

Attestations are not a substitute for due diligence, but they are often the only structured evidence buyers get before an AI platform is allowed into production. For enterprise teams, the question is less about paperwork and more about whether the vendor can prove how identities, secrets, data handling, logging, and change control are governed. That is especially important when the system will interact with privileged workflows, regulated data, or autonomous agents.

Without attestations, procurement tends to lean on sales claims and generic trust language instead of control evidence. That leaves security teams blind to the exact failure modes that matter most in production, particularly around credentials and access paths. NHI Management Group’s analysis of DeepSeek breach shows why this matters: exposed secrets and sensitive records can create immediate operational risk when identity controls are weak. The NIST Cybersecurity Framework 2.0 reinforces the need for evidence-driven governance, not assumption-driven approval. In practice, many security teams encounter weak AI governance only after an environment has already been connected to production data, rather than through intentional pre-production control validation.

How It Works in Practice

A useful attestation request should ask for evidence, not slogans. For AI in production, buyers should expect documentation that confirms who controls the model, how training or fine-tuning data is handled, how secrets are stored and rotated, what telemetry is logged, and what happens when the system fails. If the product uses agents or tool-using workflows, the attestation should also cover workload identity, runtime authorization, and whether the system can be constrained to specific tasks rather than broad standing access.

Current guidance suggests that the most useful attestations are specific to the deployment model. A SaaS model with no customer-managed keys is very different from a self-hosted agent with access to internal APIs. The buyer should therefore ask for control evidence that maps to the intended use case, not a generic trust package. That evidence can include independent security reports, control narratives, penetration test summaries, and architecture diagrams that show where identity boundaries are enforced. For identity-heavy workloads, the Ultimate Guide to NHIs — Why NHI Security Matters Now is useful context because AI systems increasingly depend on non-human credentials rather than user logins. Vendor and assessor claims should then be tested against runtime controls, because the attestation only tells part of the story.

• Require evidence for secret handling, rotation, and revocation, not only a policy statement.
• Confirm whether the AI has access to production systems through dedicated workload identity or shared credentials.
• Check whether logging is sufficient for incident response, audit, and model abuse investigation.
• Validate how privilege is limited for agents that call tools or APIs on behalf of users.

The Ultimate Guide to NHIs — The NHI Market is also a useful reference point because many “AI platform” purchases now bundle identity-sensitive components that should be reviewed like any other privileged workload. These controls tend to break down when a vendor cannot separate tenant data paths or when agentic features are allowed to call internal systems with broad, persistent credentials.

Common Variations and Edge Cases

Tighter attestation requirements often increase procurement time and legal review effort, requiring organisations to balance stronger assurance against deployment speed. That tradeoff is real, especially when the AI use case is low-risk or limited to internal experimentation. Best practice is evolving, and there is no universal standard for what a sufficient AI attestation must contain yet.

For low-impact use cases, a lightweight attestation may be enough if it confirms basic security posture and data boundaries. For high-impact use cases, especially those involving regulated data, customer-facing decisions, or autonomous agents, the bar should be much higher. In those cases, buyers should expect proof of control operation, not just policy existence. The most common gap is overreliance on vendor attestations that do not speak to the actual production architecture. NHI Management Group’s research on the LLMjacking: How Attackers Hijack AI Using Compromised NHIs threat pattern shows why this matters: compromised non-human credentials can become the fastest route into AI services. Security teams should therefore treat attestations as an input to risk acceptance, not the final control.

Related resources from NHI Mgmt Group

• What should IAM teams do before using AI for role mining?
• Why is single-provider AI agent governance not enough for enterprise security?
• What should security teams evaluate before using compound AI systems in production?
• How should security teams govern Google Vertex AI access in production environments?