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Securing an AI system on AWS means protecting the whole workload—not just the model endpoint. AWS secures the underlying cloud infrastructure; you remain responsible for the security of your configuration, data, identities, application, and operations. A service’s compliance status does not, by itself, make your application compliant.
Use the workload’s exposure, data sensitivity, model path, business impact, and regulatory obligations to decide which controls it needs. The steps below provide a baseline for generative AI and other AI/ML workloads; Bedrock-specific capabilities are identified where relevant.
What should you assess before choosing controls?
Start with a map of the system and its consequences. Record which models and AWS services it uses, who and what can invoke them, where data comes from and goes, whether the model is customized, which external systems are connected, and where the workload is deployed. Include what could happen if an answer is inaccurate, a service is unavailable, or data is disclosed or manipulated.
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AWS’s AI Security Scoping Matrix and Prescriptive Guidance on generative AI security and governance describe control selection as workload-dependent. Revisit the assessment when the users, data sources, model, integrations, deployment geography, or business purpose changes.
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| Assessment axis | What to record | Why it changes the control plan |
|---|---|---|
| Exposure | Internal-only, partner-facing, or public-facing access | Broader access can increase the number of callers and threat paths to manage. |
| Data sensitivity | Public, internal, confidential, personal, regulated, or proprietary data | Sensitive data calls for tighter access, handling, retention, and review decisions. |
| Model path | Pretrained inference, retrieval-augmented generation (RAG), fine-tuning, or other customization | Retrieval and customization introduce data and access paths that must be included in testing and governance. |
| Authorization complexity | Whether each user may access different source records | Per-user access rules must be enforced before source material is passed to the model. |
| Impact and criticality | Consequences of inaccurate, unavailable, manipulated, or disclosed outputs | Higher-impact uses may need more evaluation, monitoring, human review, and response readiness. |
| Compliance context | Applicable geography, sector, contract terms, and evidence requirements | Obligations depend on the actual processing context and customer-controlled architecture. |
| Operational maturity | Available monitoring, incident response, evaluation, and change-management capability | Controls need owners and operating procedures, not just technical configuration. |
Who owns security and compliance?
AWS’s Amazon Bedrock documentation states, “Security is a shared responsibility between AWS and you.” AWS is responsible for security of the cloud infrastructure. Customers are responsible for security in the cloud according to their service choices, data, requirements, and applicable law. The precise division varies by service and configuration, so identify which controls AWS operates and which your team must implement.
Assign named owners for model selection, approval of data use, production releases, guardrail configuration, incident review, and acceptance of residual risk. Document permitted use cases, data classifications, evaluation criteria, expectations for reviewing outputs, and escalation paths. AWS recommends policies for model access and use, recurring security assessments and compliance audits, and documentation of implemented measures.
AWS Audit Manager’s Generative AI Best Practices Framework v2 can help organize evidence for governance, privacy, accuracy, security, resilience, and related practices in supported Bedrock and SageMaker AI contexts. Evidence mapping can support assurance work; it does not certify that a particular application meets its obligations.
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Separate identities and permissions
Use narrowly scoped IAM roles for distinct activities, such as evaluation, general inference, fine-tuned-model use, and specialized access. Limit each role to the actions, resources, and conditions its task requires, and review permissions as teams, models, and workflows change. Avoid treating access to a model as equivalent to authorization to all data that might be supplied to it.
Authorize retrieval before it reaches the prompt
For RAG, check the requesting user’s permissions against source data before retrieving and inserting context into a prompt. The model should receive only information that user is allowed to access. A fluent answer can still represent a serious disclosure if retrieval crosses an authorization boundary. AWS’s May 15, 2026 article on its AI Security Framework highlights this RAG risk.
Constrain network paths
Where appropriate, consider private connectivity such as AWS PrivateLink for Bedrock or SageMaker AI invocations. Treat it as one network control, not a substitute for identity checks, ingress and egress design, or monitoring.
How do you protect AI data through its lifecycle?
Trace data from collection through preprocessing, retrieval, inference, logging, retention, and deletion. Restrict access to datasets and vector stores, encrypt relevant data at rest and in transit, and minimize the personal or proprietary information sent to models. Masking or tokenization may reduce exposure for sensitive fields when the transformation preserves the task’s usefulness.
AWS describes Bedrock as encrypting data at rest and in transit and supporting customer control of keys through AWS KMS. Confirm the current behavior for the specific service, region, feature, and configuration you deploy rather than generalizing that statement to every AI workload on AWS.
Make a deliberate decision about invocation logs
Bedrock can optionally store invocation metadata, requests, and responses in S3 or CloudWatch Logs. Such logs can help with operations, investigations, and audit evidence, but prompts and outputs may contain sensitive information. Decide what to capture, whether to enable logging, who can read the records, how they are encrypted, and how long they are retained. Apply access and retention rules to the logs as carefully as to the source data.
How do you reduce unsafe or unreliable model behavior?
Validate inputs before inference and outputs before they are used or shown. Include prompt injection, data-exfiltration attempts, harmful or disallowed content, output integrity, and application-specific rules in the threat model and evaluation plan.
For supported Bedrock use cases, Guardrails can be configured to filter inputs and outputs, including harmful categories and restricted topics. Configure and test them against the intended behavior of the application. Keep them within a layered design that also includes authorization, application-specific validation, monitoring, and human review where the consequences of an output warrant it. A guardrail alone is not a complete defense.
What monitoring and incident processes should be in place?
Use CloudTrail to track API activity. Where useful, combine CloudWatch metrics and dashboards with configured invocation logging to support operational monitoring and audit. Restrict log access and apply suitable encryption and retention controls. Audit Manager’s framework can help map evidence to recommended practices, but teams still need to determine which evidence is relevant to their obligations.
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Define response procedures and owners for unexpected disclosures, prompt attacks, policy violations, model changes, and anomalous access. Procedures should explain how to contain the affected path, preserve relevant evidence, notify stakeholders when required, and update controls. AWS’s framework examples include handling inadvertent PII disclosure and monitoring for anomalies in real time.
How do you map AWS controls to compliance obligations?
Identify the laws, standards, contracts, data-location requirements, and internal rules that apply to the specific use case. AWS states that Bedrock is in scope for several named compliance programs and is HIPAA eligible. Those service-level statements do not establish that an end-to-end customer application satisfies every obligation. Check current AWS compliance documentation for the relevant service and geography, then assess the parts of the architecture and operating process your organization controls.
For personal data, AWS’s Security Blog advises data minimization and review of privacy risks with legal counsel or a Data Protection Officer. Applicability depends on jurisdiction and processing context; do not infer a legal conclusion from a cloud service’s eligibility or compliance status.
How should the baseline change with workload risk?
Use the assessment to scale the depth of controls rather than applying one checklist identically to every system. For example, a public-facing application that processes personal information and retrieves user-specific records needs particular attention to authorization boundaries, input and output safeguards, log handling, and incident readiness. An internal application using non-sensitive data may have a different exposure and evidence burden, but still needs accountable access, data handling, and operational ownership.
Reassess the control plan when a workload becomes more exposed, handles more sensitive data, adds retrieval or model customization, or takes on greater business impact. The appropriate controls depend on those changes and on the obligations that apply to the deployment.
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