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Do not approve an AI model for production until its data, weights, interfaces, infrastructure, identities, monitoring and response procedures have passed one risk-based release review. Use the NIST AI Risk Management Framework (AI RMF) to define and prioritize risk, OWASP Secure AI/ML Model Ops practices to implement controls, CISA/NSA guidance for externally developed systems, and OWASP AISVS requirements to produce test evidence. The model is only one part of the security boundary: a trustworthy deployment also protects training data, prompts, vector stores, tools, APIs, containers, accelerators, logs and people.
Start with one security boundary and a release decision
AI systems combine conventional software risks with threats that are specific to machine learning. An attacker may poison training data, extract model behavior or weights, infer whether a person’s data was used, inject instructions through a prompt, exhaust an inference budget, or exploit a malicious model file. Confidentiality, integrity and availability apply to the model and to the data used to train, tune and query it.
Write a deployment threat model before selecting controls. Inventory the assets and draw trust boundaries around each data flow:
- Training, fine-tuning and evaluation datasets, including personal, confidential and regulated data.
- Model weights, checkpoints, tokenizer files, configuration, prompts and system instructions.
- Inference and administration APIs, webhooks, queues and client applications.
- Identities, service accounts, signing keys, secrets, registries and CI/CD systems.
- Tools, plugins, vector stores, retrieval indexes, GPUs, containers and host machines.
- Request records, telemetry, audit logs, backups and exported evaluation results.
For every asset, record an owner, sensitivity, integrity requirement, availability target, retention period and recovery method. Then map each threat to a preventive control, a test and an accountable person. A release is not ready when a checklist is merely marked “done”; it is ready when the evidence shows that the remaining risk is understood and accepted.
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1. Prove training-data integrity and privacy
Trace provenance and prevent poisoning
Keep an immutable record of where every training, fine-tuning and validation dataset came from, when it was collected, which transformation code produced it and who approved it. Version the raw and derived data separately so a suspicious transformation can be reproduced and removed. Validate schemas, encodings, labels, ranges and file types before data enters a training job. Deduplicate samples and compare distributions with an approved baseline; unexpected changes should block promotion for investigation.
Data poisoning is the deliberate insertion of data intended to alter model behavior. Validation, independent review, auditability and reproducible pipelines reduce that risk, but they do not prove that a dataset is benign. For high-impact use cases, test the model against curated clean sets and adversarially altered sets, and retain the results with the dataset version.
Minimize sensitive data
Classify personal, confidential and regulated fields before training. Remove fields that are not needed, redact identifiers, and use access-controlled feature stores or protected enclaves for data that must remain. Define retention and deletion procedures for raw data, intermediate artifacts, caches and logs. Test that deletion requests propagate to every copy and that the deployment does not return sensitive training examples in ordinary prompts.
Release evidence
- Signed dataset manifest with source, version, checksum and approval.
- Validation and poisoning-detection results, including exceptions.
- Privacy review documenting minimization, retention and deletion tests.
- Reproducible pipeline definition and an identified rollback dataset.
2. Secure the model and dependency supply chain
Verify models before they run
Treat model files as executable production artifacts. Record the publisher, build process, intended architecture, license, checksum and evaluation results. Obtain artifacts through an access-controlled registry, verify a digital signature or an independently validated digest, and reject unsigned or altered files. Do not load a third-party checkpoint solely because its name or download count appears trustworthy.
Unsafe deserialization can execute code when formats such as .pt or .pkl are loaded. Prefer formats and loaders that do not execute arbitrary code, run conversion in an isolated environment, and scan the resulting artifact before it reaches a training or inference host. Restrict registry write access and require review for replacement of an existing version.
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Scan the complete dependency graph
Generate a lockfile or equivalent bill of materials for application packages, operating-system images, CUDA or accelerator libraries, serving frameworks and build tools. Run dependency, container and configuration scans in CI/CD. Pin versions, monitor advisories and define a patch deadline for exploitable findings. Separate build credentials from runtime credentials so a compromised dependency cannot publish a replacement model.
Encrypt and control artifacts
Encrypt weights, datasets, feature stores, logs and backups in transit and at rest. Keep encryption keys in a managed key system with rotation and access logs. The registry should expose only the versions and metadata each job needs; training, deployment and inference identities should not share broad administrative permissions.
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Scan source code, notebooks, container layers, model prompts and CI logs for API keys, tokens, private keys and connection strings. Revoke any credential that has appeared in a repository or log, then issue a replacement through a secret manager. Ensure build output and exception traces cannot print secret values.
Require authentication for both the inference plane and the management plane. Authorization must be checked server-side for every request, not inferred from a client-supplied role or tenant identifier. Use separate identities for data preparation, training, registry publishing, deployment, inference, monitoring and incident response. Grant each identity only the actions and data it needs, with short-lived credentials where practical.
Test negative cases: a user from tenant A must not retrieve tenant B’s prompt, vector record, usage data or model; an inference identity must not alter a registry; and a staging identity must not invoke production tools. Review permissions after team changes and when a model is retired.
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4. Harden the inference API and input path
Separate instructions from user data
Prompt injection occurs when untrusted content persuades a model to ignore its intended instructions or disclose data. Keep system instructions, developer policy and user content in distinct, typed fields. Treat retrieved documents, web content, files and tool output as untrusted data. Enforce authorization and data filtering outside the model; do not rely on a refusal phrase as the access-control mechanism.
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Limit abuse and resource consumption
Authentication alone does not stop denial of service or runaway cost. Apply per-tenant and per-identity limits for requests, tokens, concurrency, latency and spend. Add quotas to batch and streaming paths, enforce maximum context sizes, and reject repeated abusive patterns. Rate-limit management endpoints more aggressively than ordinary inference endpoints and record rejected requests for investigation.
Build an adversarial test set containing instruction-conflict prompts, encoded payloads, malicious retrieved text, oversized inputs, malformed files and cross-tenant access attempts. A release should specify expected behavior and an escalation path for every failed case.
5. Add hard controls to agents and tools
Tool-using and agentic systems create additional paths from model output to real-world effects. Give each tool a narrow schema, explicit authorization and an allowlist of destinations and operations. Validate arguments after the model proposes them and before execution. Require user confirmation for irreversible actions such as sending messages, changing records, spending funds or deleting data.
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Cap recursion, retries, chain depth, parallel tool calls and total execution time. Set budgets for tokens and external calls. Circuit breakers or kill switches should stop a workflow when cost, latency, error rate or tool-call volume exceeds a defined threshold. Exercise those breakers in a staging environment and record how quickly queued work is halted and credentials are contained.
6. Isolate runtime infrastructure and tenants
Harden container images, remove unnecessary packages, run as a non-root user, drop unused Linux capabilities and use read-only filesystems where possible. Enforce network egress policies so an inference process can contact only approved services. Scan images and infrastructure definitions in CI/CD, and block deployment when critical findings lack an approved exception.
Separate tenants at the application, storage, network and accelerator layers. Do not assume that a tenant identifier in a prompt provides isolation. Use dedicated namespaces, queues and encryption contexts; clear GPU memory and temporary files between jobs; and restrict access to host interfaces. Evaluate side-channel exposure when accelerators or hosts are shared, especially for sensitive workloads.
7. Monitor behavior, drift and forgotten endpoints
Collect near-real-time telemetry for request volume, token usage, spend, latency, errors, safety events, tool calls and data-distribution changes. Correlate events with tenant, identity, model version and deployment region while minimizing sensitive content in logs. Protect audit logs from alteration and define retention, access and deletion rules.
Set thresholds before launch. Examples include a sudden increase in rejected prompts, an unexpected token-to-request ratio, a new geographic source, a rise in tool failures, or a shift in input features away from the validation distribution. Alert owners, preserve relevant evidence and test that an alert reaches someone who can act.
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Scan DNS, API gateways, service registries and cloud accounts for obsolete test, staging and preview endpoints. Remove them or place them behind authentication and network controls. An unmaintained endpoint with an old model or debugging flag is part of the production attack surface.
8. Prepare incident response, governance and retirement
Assign an incident commander, technical owner, privacy contact and communications path before launch. Document severity criteria, evidence-preservation steps, notification obligations, rollback procedures and the conditions for activating a kill switch. CISA’s joint deployment guidance organizes operational protection around protecting, detecting and responding to malicious activity in externally developed AI systems.
Maintain a rollback artifact and a known-good configuration. Test restoration of the model, prompts, dependencies, data indexes and keys—not just the application container. Define how to quarantine a poisoned dataset, revoke a compromised model signature, disable a tool, rotate credentials and communicate a service limitation.
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Use the standards together, not as substitutes
| Resource | What it contributes | When to use it |
|---|---|---|
| NIST AI RMF 1.0 | Voluntary risk-management structure for incorporating trustworthiness into AI design, development, use and evaluation. | Define outcomes, owners, risk tolerance and governance across the lifecycle. Released January 26, 2023; record the version because NIST says the framework is being revised. |
| NIST Generative AI Profile (AI 600-1) | Generative-AI-specific risk considerations that extend the RMF. | Use alongside the RMF for systems that generate text, code, images, audio or tool actions. Released July 26, 2024. |
| NIST security and resilience research | Explains confidentiality, integrity and availability risks affecting systems, training data and outputs, while noting that existing guidance does not cover every evasion, extraction, membership-inference or availability risk. | Use to identify residual threats that require testing or compensating controls. |
| CISA/NSA joint deployment guidance | Deployment and operations practices for externally developed AI systems, including known-vulnerability mitigation and protect, detect and respond activities. | Apply when a supplier, open-source project or another business unit built the model or serving stack. |
| OWASP Secure AI/ML Model Ops | Concrete practices for poisoning, malicious model files, secrets, APIs, agents, isolation, monitoring and orphan cleanup. | Translate risks into engineering controls and operational tests. |
| OWASP AISVS 1.0 | 191 testable requirements in 12 chapters and three appendices, organized into three verification levels. | Use requirement IDs as CI/CD acceptance criteria and audit evidence. Level 1 is a baseline, Level 2 is intended for production and sensitive-data systems, and Level 3 targets high-assurance or critical environments. Released June 2026. |
For most production services, select AISVS Level 2 unless a documented risk assessment justifies another target. Safety-sensitive or critical environments may require Level 3. Record the AISVS version and each applicable requirement ID; a control that cannot be tied to a test, owner and artifact is not verifiable.
Run a repeatable pre-deployment verification workflow
- Set scope and risk. Approve the asset inventory, data classifications, users, tenants, regions, model capabilities, tools and availability objectives. Record the NIST RMF and profile versions used.
- Build the threat model. Cover poisoning, prompt injection, extraction, membership inference, malicious files, credential theft, denial of service, cross-tenant access, supply-chain compromise and unsafe tool actions.
- Choose the assurance target. Select an AISVS level and list the applicable requirement IDs. Obtain risk-owner approval for any exclusion.
- Automate pipeline checks. Run secret, dependency, container, infrastructure and license scans; verify dataset and model signatures; enforce reproducible builds; and block unsigned artifacts.
- Test authorization and isolation. Exercise every inference and management role, tenant boundary, registry permission, storage policy, network egress rule and accelerator-sharing control with both allowed and denied cases.
- Attack the interface. Run prompt-injection, adversarial-input, malformed-file, context-overflow, rate-limit, quota, spend and abuse tests. Test agent recursion caps, tool allowlists and kill switches.
- Validate operations. Confirm telemetry, drift alerts, log protection, on-call escalation, rollback, key rotation, endpoint discovery and evidence retention in a production-like environment.
- Hold the go/no-go review. List unresolved findings, exploitability, affected assets, compensating controls, monitoring thresholds, incident contacts and the retirement plan. A named risk owner must accept every exception.
Common release failures and corrective actions
| Failure | Why it is unsafe | Correction before approval |
|---|---|---|
| “The API requires a login, so it is secure.” | Authenticated users can still inject prompts, exhaust budgets or access another tenant’s data. | Add server-side authorization, input and output validation, quotas, rate limits and abuse detection; test denied cases. |
| “The model came from a trusted source.” | A replacement file, dependency or unsafe loader can compromise the runtime. | Verify provenance and signatures, scan dependencies, use safe loading, and restrict registry writes. |
| “The model refused our sample attack.” | Behavior can change with retrieved text, encoding, tools, versions or a new dataset. | Maintain adversarial regression tests and enforce controls outside the model. |
| “Monitoring will be added after launch.” | Without telemetry and thresholds, theft, drift and runaway spend may persist unnoticed. | Make dashboards, alerts, protected logs and response drills release gates. |
| “Staging is separate, so old endpoints do not matter.” | Forgotten previews often retain weaker credentials and outdated dependencies. | Inventory and remove or lock down every endpoint, identity and registry path. |
Release evidence to retain
- Threat model, asset inventory, data-flow diagram and risk-owner approvals.
- Dataset and model manifests, signatures, checksums, software bill of materials and scan results.
- Authorization, tenant-isolation, adversarial-input, rate-limit, quota and agent-control test reports.
- Container, infrastructure and secret-scan evidence, with time-bounded exceptions.
- Monitoring dashboards, alert thresholds, protected-log configuration and on-call acknowledgements.
- Rollback, kill-switch, credential-rotation and retirement exercise records.
- Final AISVS level, version, requirement IDs and the signed go/no-go decision.
A secure deployment is therefore a maintained system of controls, tests and ownership—not a one-time model review. Re-run the relevant checks whenever weights, data, prompts, dependencies, tools, infrastructure, tenants or usage limits change.
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