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Every AI deployment should have accountable owners, an inventory of its models, data and connections, a risk assessment, least-privilege access, data and secret protections, secure development and supply-chain practices, pre-release testing, production monitoring, incident response, recovery procedures and regular reassessment. The depth of each control depends on the system’s data, exposure, authority and potential impact: a model that can retrieve sensitive records or take actions needs stronger safeguards than one that only makes low-impact predictions.
Why AI security starts with ordinary system security
An AI feature is part of a system, not a separate security boundary. Its application, model, data, dependencies, infrastructure, interfaces and connected services can all affect confidentiality, integrity and availability. A secure model does not make an insecure application safe: access checks, stored data, credentials, APIs, deployment configuration and operational processes still matter.
NIST makes this overlap explicit: “Some cybersecurity risks related to AI systems are common (or identical) to cybersecurity risks across software development and deployment.” Its AI security and resilience overview describes this shared security foundation and discusses protecting AI systems across their lifecycle. The practical implication is to apply established software and infrastructure protections, then add controls for the particular risks created by the model’s inputs, outputs, training or retrieval data, providers and capabilities.
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Establish ownership and an accurate system inventory
Assign decision-makers
Name both a business owner and a technical owner. The business owner should define the purpose, acceptable uses and consequences of failure; the technical owner should be accountable for implementation, access, deployment and maintenance. Identify who approves changes, accepts residual risk, receives incident escalations and can disable the system. Without named responsibility, it is easy for a model, data source or integration to change without anyone reassessing its security.
Record what is actually deployed
Keep an inventory that describes the full path from user input to model output and any resulting action. Record:
- The system’s purpose, users, allowed uses and prohibited uses.
- The model and provider, including versions where known, and how the model is hosted.
- Data sources, datasets, retrieval stores, and whether submitted information may be retained or used for training.
- Connected tools, APIs, plugins or other services, along with the permissions they receive.
- Application components, dependencies, infrastructure and administrative interfaces.
- Where data and services cross organizational or geographic boundaries, if relevant to the deployment.
Use the inventory to track changes, not just to document a launch. A new provider, model version, data source, permission or tool can change the threat picture and should follow a defined change-approval path.
Assess risk before choosing control depth
Start with what the system can see and do, who can reach it, and what happens if it is manipulated, wrong or unavailable. Map assets and trust boundaries; consider likely threat actors, sensitive data, exposed interfaces, misuse cases and model-specific attacks. Then record the likely impact and the controls that address it. Revisit the assessment when prompts, models, data, integrations or permissions change.
Use these dimensions to decide where stronger assurance is warranted:
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- Data sensitivity and retention: Is the system exposed to personal, confidential, regulated or otherwise sensitive information? How long is it kept, and where?
- Exposure and users: Is it available to the public, limited to authenticated employees, or restricted to a controlled workflow? Can users submit arbitrary content?
- Authority and autonomy: Does it only produce suggestions, or can it retrieve records, call tools, change data, send messages or otherwise cause side effects? Can a person review the action in time?
- Provider and dependency control: Which components are managed by the organization, and which depend on external providers or updates?
- Impact of failure: Could an incorrect, manipulated or unavailable output affect people, sensitive operations or consequential decisions?
- Applicable obligations: Which jurisdiction, sector rules and system classifications apply? These depend on the actual deployment and cannot be settled by a general AI checklist.
Increase assurance when the system reaches sensitive information, acts on external systems, informs consequential decisions or operates without timely human review. Document why the selected safeguards are adequate and who accepts any remaining risk. OWASP’s Artificial Intelligence Security Verification Standard (AISVS) organizes verification by lifecycle and risk profile; OWASP says most production systems should aim for at least Level 2. Treat that as OWASP guidance, not a legal mandate or a universal requirement, and check the live standard for its current levels and control wording.
Control access, data and the AI supply chain
Apply least privilege to every component
Authenticate users and services, and give each only the access required for its task. This applies not only to the model endpoint, but also to administrative functions, datasets, vector stores, tools, cloud resources and deployment pipelines. Separate duties where appropriate, protect credentials, review access regularly and remove access when it is no longer needed. Do not give a model or agent broad credentials simply because doing so makes an integration easier.
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Protect data throughout its lifecycle
Classify the information the system handles and minimize what it receives. Before sending information to an external model provider, determine what data is necessary and understand the provider’s handling, retention and training-use terms. Protect data in transit and at rest; define retention and deletion rules for prompts, outputs, retrieval content and logs. Validate data provenance and permissions so that a system does not expose information it was never authorized to use.
Retrieved documents and user-supplied content should be treated as untrusted input, not as instructions with authority. A malicious or misleading document can influence a model’s response or attempt to redirect a connected tool. Input and output checks can help within a broader design, but do not treat a filter or the model’s own assurances as proof that an unsafe instruction cannot succeed.
Secure software, models and third-party services
Use secure development and deployment practices for the complete application. Track software dependencies and the provenance of models and data; restrict who can introduce or replace artifacts, and validate changes before release. Review third-party terms, security evidence, incident practices and update processes for providers and integrations. A supplier’s model evaluation does not establish that your application, authorization rules, prompts, data flows and tools are secure.
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For generative AI, third-party due diligence includes understanding how an external service handles submitted data, what other services process it, how incidents are communicated and how changes are made. NIST’s Generative Artificial Intelligence Profile (NIST AI 600-1), released July 26, 2024, includes actions relating to data protection and retention, secure development, monitoring, testing, incident response and third-party considerations.
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Test the system people will use, not only the underlying model. A model-level evaluation cannot show whether an application enforces authorization, limits a tool’s permissions, protects retrieved records or recovers safely when a provider fails. Include tests that reflect the actual interfaces, data, integrations and operating conditions.
A pre-release plan should cover:
- Representative use: Typical inputs, expected outputs and the user groups that will use the system.
- Authorization boundaries: Whether users can access only the records, functions and tools their role permits, including through indirect or unusual requests.
- Abuse and adversarial cases: Attempts to extract protected information, manipulate instructions, misuse functionality or trigger actions outside the intended purpose.
- Data exposure: Whether prompts, outputs, retrieval results, errors or logs reveal information that should remain private.
- Tool permissions: Whether a tool can perform only its intended operation and whether approval is required for higher-impact actions.
- Failure and recovery: What happens when the model, provider, data source or connected service is unavailable, returns an error or behaves unexpectedly.
Record findings, unresolved issues, mitigations and the release decision. Set a threshold for when a finding blocks release or requires an explicit risk acceptance. Repeat relevant tests after meaningful changes: a new model, prompt, integration, dataset or permission can invalidate earlier results.
Protect the running system and make activity observable
Limit what the system can do
Use network boundaries, rate limits and resource limits appropriate to the application. Validate inputs and outputs where that helps enforce the system’s intended use, and constrain tool execution to narrowly defined operations. For tools that can change records, communicate externally or otherwise create consequential side effects, use authorization and human approval controls appropriate to the impact. Do not rely on a model’s claim that it will behave safely as the control over its authority.
Log enough to investigate, without creating a new data exposure
Keep privacy-conscious security logs that can help investigators understand access, configuration changes, errors and incidents. Consider which events are necessary to reconstruct what happened and which prompt or output content is sensitive and should not be retained by default. Restrict log access, protect logs against unauthorized changes and define how long they are kept. Alert on suspicious patterns relevant to the system, such as unusual access or unexpected tool activity.
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Monitoring complements pre-release testing; it does not replace it. A one-time test cannot establish continuing security as models, prompts, tools, data and dependencies evolve. Monitoring should help teams detect changes in operation and investigate whether safeguards continue to work.
Define human oversight and acceptable use
Tell users what the system is intended to do, what uses are prohibited and what to do when an output is uncertain, harmful or inconsistent with policy. Assign responsibility for reviewing outputs in workflows where review is needed, and specify who can override or stop the system. Human review is not a universal cure: reviewers need enough context, authority and time to identify problems, and the system should not make consequential actions effectively irreversible before review can occur.
For generative AI, explicitly define human and AI responsibilities. Decide which outputs require review, whether sensitive information may be submitted, and which actions the model may take without approval. These rules should match the system’s actual permissions and workflow rather than relying on a general reminder to use judgment.
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Prepare incident playbooks for the ways an AI deployment can fail or be compromised. Include data exposure, compromised credentials, unsafe outputs, provider incidents, model or dependency compromise and loss of service. For each scenario, identify who is responsible, how to contain the issue, how to preserve evidence and when to notify affected parties under applicable obligations.
Decide in advance how to disable the system or a specific integration, revoke credentials, roll back a change, restore service and recover data. Test the recovery approach where the system’s impact warrants it; a documented plan that cannot be carried out under pressure is not enough. When retiring a system, remove its credentials, data, integrations and access paths, and update the inventory so unused components do not remain available.
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Use frameworks as guides, not substitutes for judgment
NIST’s AI Risk Management Framework 1.0 organizes risk work around Govern, Map, Measure and Manage. NIST describes it as intended for voluntary use, and its framework page says it is being revised; check the current NIST AI RMF page for status rather than assuming 1.0 is the newest edition. The AI RMF Core describes the functions as a lifecycle approach: establish governance, understand context, measure risks and manage them over time.
For generative AI-specific risk actions, NIST AI 600-1 is a profile that applies the RMF to generative AI; it is guidance, not a universal legal checklist. NIST’s SP 800-53 Control Overlays for Securing AI Systems (COSAiS) project uses SP 800-53 controls to develop implementation-oriented overlays for AI system types and components. The project page, updated January 8, 2026, lists assistant/LLM use, predictive AI, agents and AI developer security among its use cases; check that page for whether final overlays have been published before relying on implementation details.
CISA and the UK National Cyber Security Centre announced their joint Guidelines for Secure AI System Development on November 26, 2023. They span secure design, development, deployment and operation. They provide a foundational lifecycle perspective, while newer AI-specific guidance can help address developments in generative AI and related deployments. None of these frameworks makes a control legally mandatory for every organization; determine obligations for the actual jurisdiction, sector and system classification.
Keep the baseline effective as the system changes
Security is a continuing operating responsibility, not a launch gate completed once. Reassess after changes to the model, provider, prompts, data, integrations, user population, permissions or business purpose. Monitor whether controls are working, record accepted residual risks, and remove capabilities that no longer serve a need. A deployment is only as well protected as its current configuration and the people responsible for maintaining it.
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