Healthcare and life sciences organizations are transforming drug discovery, medical devices, and patient care with generative AI agents. In regulated industries, any system that impacts product quality or patient safety must comply with GxP (Good Practice) regulations, such as Good Clinical Practice (GxP), Good Laboratory Practice (GLP), Good Manufacturing Practice (GMP). Organizations must demonstrate to regulatory authorities that their AI agents are safe, effective, and meet quality standards. Building AI agents for these GxP environments requires a strategic approach that balances innovation, speed, and regulatory requirements.
AI agents can be built for GxP environments: The key lies in understanding how to build them appropriately based on their risk profiles. Gen AI introduces unique challenges around explainability, probabilistic outputs, and continuous learning that require thoughtful risk assessment rather than blanket validation approaches. The disconnect between traditional GxP compliance methods and modern AI capabilities creates barriers to implementation, increases validation costs, slows innovation speed, and limits the potential benefits for product quality and patient care.
The regulatory landscape for GxP compliance is evolving to address the unique characteristics of AI. Traditional Computer System Validation (CSV) approaches, often with uniform validation strategies, are being supplemented by Computer Software Assurance (CSA) frameworks that emphasize flexible risk-based validation methods tailored to each system’s actual impact and complexity (FDA latest guidance).
In this post, we cover a risk-based implementation, practical implementation considerations across different risk levels, the AWS shared responsibility model for compliance, and concrete examples of risk mitigation strategies.
Risk based implementation framework
Effective GxP compliance for agentic AI systems require assessing risk based on operational context rather than technology features alone. To support risk classification, the FDA’s CSA Draft Guidance recommends evaluating intended uses across three factors: severity of potential harm, probability of occurrence, and detectability of failures.
In Figure 1, this assessment model combines traditional operational roles with modern risk-based levels. Organizations should assess how AI agents function within workflows and their potential impact on regulated processes.
Figure 1. GxP compliance for AI agents combines traditional Role-based with CSA’s modern risk-based levels
The same AI agent capability can warrant dramatically different validation approaches depending on how it is being deployed. How is the agentic AI being consumed and within existing GxP processes? What is the level of human oversight or human-in-the-loop controls? Is the AI agent itself being added as an additional control? What is the potential impact of AI failures on product quality, data integrity, or patient safety?
Consider an AI agent for scientific literature review. When creating literature summaries for internal team meetings, it presents low risk, requiring minimal controls. When scientists use these insights to guide research direction, it becomes medium risk, needing structured controls, such as human review checkpoints. When supporting regulatory submissions for drug approval, it becomes high risk and requires comprehensive controls because outputs directly impact regulatory decisions and patient safety.
This risk-based methodology allows organizations to balance innovation with compliance by tailoring validation efforts to actual risk levels rather than applying uniform controls across all AI implementations.
Implementation considerations
Successful AI agent designs require common controls that apply consistently across risk levels for quality and safety. Organizations should maintain clear records of AI decisions, prove data has not been altered, reproduce results when needed, and manage system updates safely. AWS supports these requirements through qualified infrastructure and various compliance certifications such as ISO, SOC, and NIST. For a more complete list, see our Healthcare & Life Sciences Compliance page. Detailed compliance validation information for Amazon Bedrock AgentCore is available in the compliance documentation. To implement these controls effectively, organizations can refer to the National Institute of Standards and Technology (NIST) AI Risk Management Framework for AI-risk guidance and ALCOA+ principles to promote data integrity.
Shared responsibility model
Successful generative AI cloud-implementation in GxP environments requires understanding the shared division of responsibilities between customers and AWS, as outlined in the Shared responsibility model, to allow organizations to focus on delivering effective and compliance-aligned solutions.
As AWS helps protect the infrastructure that runs the services offered in the AWS Cloud, Table 1 provides practical examples of how AWS can support customers in validating their agentic AI systems.
| Focus | Customer responsibilities | How AWS supports |
| Validation strategy | Design risk-appropriate validation approaches using AWS services for GxP compliance. Establish acceptance criteria and validation protocols based on intended use. |
Inherit compliance controls with AWS services such as Amazon Bedrock’s ISO 27001, SOC 1/2/3, FedRAMP, and GDPR/HIPAA eligibility. Support your GxP training requirements through AWS Skill Builder for artificial intelligence and machine learning (AI/ML) and AWS Certified Machine Learning – Specialty. Use infrastructure as code through AWS CloudFormation to support on demand validations and deployments that provide repeatable IQ for your agentic workloads. |
| GxP procedures | Develop SOPs that integrate AWS capabilities with existing quality management systems. Establish documented procedures for system operation and maintenance. |
Build GxP agentic systems with HCLS Landing Zones, designed to align for highly regulated workloads, this capability can augment and support your standard procedure requirements. Augment risk management procedures with Amazon Bedrock AgentCore supporting end-to-end visibility and runtime requirements for complex multi-step tasks. Use AWS Certified SysOps Administrator and AWS Certified DevOps Engineering certifications for training requirements and to make sure teams can operationalize and govern procedural compliance on AWS. |
| User management | Configure IAM roles and permissions aligned with GxP user access requirements. Maintain user access documentation and training records. | Secure AI agents access with AWS IAM and Amazon Bedrock AgentCore Identity to establish fine-grained permissions and enterprise identity integration and use IAM Identity Center to streamline workforce user access. |
| Performance criteria | Define acceptance criteria and monitoring thresholds for gen AI applications. Establish performance monitoring protocols. |
Use Amazon Bedrock Provision Throughput plan for agentic workflows that require consistent and guaranteed performance requirements. Monitor performance with Amazon Bedrock AgentCore Observability and with Amazon CloudWatch with customizable alerts and dashboards for end-to-end visibility. |
| Documentation | Create validation documentation demonstrating how AWS services support GxP compliance. Maintain quality system records. | Use AWS Config to help generate compliance reports of your agentic deployments with conformance packs for HIPAA, 21 CFR Part 11, and GxP EU Annex 11.Store your GxP data with Amazon Simple Storage Service (Amazon S3), which offers enterprise-grade 11 nines of durability with support for versioning and user defined retention policies. |
| Provenance | Monitor model versions while maintaining validated snapshots. Version-control prompt templates to facilitate consistent AI interactions, track changes, and maintain records for audit trails version-control prompt templates. Lock tool dependencies in validated environment. |
Control models and data with Amazon Bedrock configurable data residency and immutable model versioning. AWS Config executes automated configuration tracking and validation. AWS CloudTrail captures comprehensive audit logging. Deploy reproducibility of AI behaviors using model versioning in AWS CodePipeline, AWS CodeCommit, and Amazon Bedrock. |
The following is an example of what customers might need to implement and what AWS provides when building AI agents (Figure 2):
Figure 2. Gen AI implementation in GxP environments requires understanding the division of responsibilities between customers and AWS.
Let’s demonstrate how these shared responsibilities translate into actual implementation.
Provenance and reproducibility
AWS Supports the following:
- Amazon Bedrock – Provides immutable model versioning, facilitating reproducible AI behavior across the system lifecycle.
- AWS Config – Automatically tracks and validates system configurations, continuously monitoring for drift from validated baselines.
- AWS CloudTrail – Generates audit trails with cryptographic integrity, capturing model invocations with complete metadata including timestamps, user identities, and model versions. Infrastructure as Code support through AWS CloudFormation enables version-controlled, repeatable deployments.
Customer responsibility: Organizations must version-control their infrastructure deployments, their prompt templates to make sure there is consistent AI behavior and maintain audit trails of prompt changes. Tool dependencies must be tracked and locked to specific versions in validated environments to help prevent unintended updates that could affect AI outputs.
Observability and performance metrics
AWS supports the following:
- Amazon Bedrock AgentCore – Provides a comprehensive solution for the unique risks that agentic AI introduces, including end-to-end visibility into complex multi-step agent tasks and runtime requirements for orchestrating reasoning chains. Amazon Bedrock AgentCore Observability captures the complete chain of decisions and tool invocations, so that you can inspect an agent’s execution path, audit intermediate outputs, and inspect failures. The Bedrock Retrieval API for Amazon Bedrock Knowledge Bases enables traceability from retrieved documents to AI-generated outputs.
- Amazon CloudWatch – Delivers real-time monitoring with customizable alerts and dashboards, aggregating performance metrics across the agent invocations. Organizations can configure logging levels based on risk, such as basic CloudTrail logging for low-risk applications, detailed AgentCore traces for medium risk, and complete provenance chains for high-risk regulatory submissions.
Customer responsibility: Organizations define acceptance criteria and monitoring thresholds appropriate to their risk level—for example, citation accuracy requirements for our literature review agent. Teams must decide when human-in-the-loop triggers are required, such as mandatory expert review before AI recommendations influence research decisions or regulatory submissions.
User management, session isolation, and security
AWS Supports the following:
- Amazon Bedrock AgentCore – Provides session isolation using dedicated microVMs, that help prevent cross-contamination between different projects or regulatory submissions. The service supports VPC endpoints to establish private connections between your Amazon VPC and Amazon Bedrock AgentCore resources, allowing for inter-network traffic privacy. All communication with Amazon Bedrock AgentCore endpoints uses HTTPS exclusively across all supported regions, with no HTTP support, so that all communications are digitally signed for authentication and integrity.
Amazon Bedrock AgentCore maintains robust encryption standards with TLS 1.2 minimum requirements (TLS 1.3 recommended) for all API endpoints. Both control plane and data plane traffic are encrypted with TLS protocols and restricted to minimum TLS 1.2 with no unencrypted communication permitted. Amazon Bedrock AgentCore Identity addresses identity complexity with a secure token vault for credentials management, providing fine-grained permissions and enterprise identity integration.
AWS Identity and Access Management (IAM) enables organizations to configure role-based access controls with least-privilege principles. Built-in encryption facilitates data protection both in transit and at rest, while network isolation and compliance certifications (SOC, ISO 27001, HIPAA) support regulatory requirements. Amazon Bedrock offers configurable data residency, allowing organizations to specify regions for data processing.
Customer responsibility: Organizations configure IAM roles and policies aligned with GxP user access requirements, facilitating least-privilege access and proper segregation of duties. Access controls must be documented and maintained as part of the quality management system.
GxP controls for AI agents
The implementation of GxP risk controls for AI agents can be considered through three key phases.
Risk Assessment evaluates the GxP workload against the organization’s risk-based validation framework. Continual quality assurance is maintained through structured feedback loops, ranging from real-time verification (see Continuous Validation) to bi-annual reviews. This process makes sure reviewers are trained against the evolving AI landscape, adapt to user feedback, and apply appropriate intervention criteria. In practice, risk assessments define risk categories and triggers for reassessment.
Control Selection is carefully selecting minimum required controls based on the 1. risk classification, 2. the specific design attributes, and 3. operational context of the AI agents. This targeted, risk-adjusted approach, makes sure controls align with both technical requirements and compliance objectives. In practice, risk categories drive required and selectable controls. An example of medium risk might require Agent and Prompt Governance controls along with two or more Detective Controls, while a high risk might require Traditional Testing (IQ, OQ, PQ) control, and two additional corrective controls.
Continuous Validation is an approach that includes the traditional fit-for-intended-use validation and subsequent process that leverages real-world data (RWD), such as operational logs and/or user feedback, to create supplemental real-world evidence (RWE) that the system maintains a validated state. As a control mechanism itself, the Continuous Validation approach helps address modern cloud-based designs including SaaS models, model drifts, and evolving cloud infrastructure. Through ongoing monitoring of performance and functionality, this approach helps maintain system GxP compliance while supporting regulatory inspections. In practice, for low-risk categories, this might be a user compliance-aligned portal that tracks user issue trends to high-risk systems that incorporate periodic self-tests with compliance reports.
The following table provides examples of Preventive, Corrective, and Detective Controls for agentic AI systems that could be incorporated in a modern GxP validation framework.
| Control element | Supporting AWS services |
| Preventive Controls | |
| Agent Behavior Specification | Use Amazon Bedrock Model Catalog to find the models that help meet your specific requirements and use AWS service quotas (limits) and documentation on service features to define supported and verifiable agent capabilities. |
| Threat Modeling | Use AWS Well-Architected Framework (Security Pillar) tools and AWS service security documentation to proactively identify AI-specific threats like Prompt Injection, Data Poisoning, and Model Inversion, and help design preventive mitigations using AWS services. |
| Response Content and Relevance Control | Use Amazon Bedrock Guardrails to implement real-time safety policies for large language models (LLMs) to deny harmful inputs or responses. Guardrails can also define denylists and filter for PII. Use Amazon Bedrock Knowledge Bases or AWS purpose-built vector databases for RAG to provide controlled, current, and relevant information to help prevent factual drift. |
| Bias Mitigation in Datasets | Amazon SageMaker Clarify provides tools to run pre-training bias analysis of your datasets. For agents, this helps make sure the foundational data doesn’t lead to biased decision-making paths or tool usage. |
| Agent & Prompt Governance | Amazon Bedrock agents and prompt management features support lifecycle processes including creation, evaluation, versioning, and optimization. The features also support advanced prompt templates, content filters, automated reasoning checks, and integration with Amazon Bedrock Flows for more secure and controlled agentic workflows. |
| Configuration Management | AWS provides an industry leading suite of configuration management services such as AWS Config and AWS Audit Manager, which can be used to continuously validate agentic GxP system configurations. AWS SageMaker Model Registry manages and versions trained machine learning (ML) models for controlled deployments. |
| Secure AI Development | Amazon Q Developer and Amazon Kiro provide AI-powered code assistance that incorporate security best practices and AWS Well-Architected principals for building and maintaining agentic workloads securely from the start. |
| AI Agents as Secondary Controls | Use Amazon Bedrock AgentCore and your data to quickly incorporate AI agents into existing GxP workflows as secondary preventative controls to add capabilities like trend analysis, automated inspections, and systems flow analysis that can trigger preventative workflow events. |
| Detective Controls | |
| Traditional Testing (IQ, OQ, PQ) | Use AWS Config and AWS CloudFormation for IQ validation by tracking resource deployment configurations. Use AWS CloudTrail and AWS CloudWatch for sourcing events, metrics, and log test results for OQ/PQ validation. |
| Explainability Audits & Trajectory Reviews | Amazon SageMaker Clarify generates explainability reports for custom models. Amazon Bedrock Invocation Logs can be used to review reasoning or chain of thought to find flaws in an agent’s logic. Utilize Amazon AgentCore Observability to look at agent invocation sessions, traces and spans. |
| Model & I/O Drift Detection | For custom models, Amazon SageMaker Model Monitor, can detect drift in data and model quality. For AI agents using commercial LLMs, use the observability service of Amazon Bedrock AgentCore to design monitoring of Inputs (prompts) and Outputs (responses) to detect concept drift. Use Amazon CloudWatch alarms to manage compliance notifications. |
| Performance Monitoring | Agentic workloads can use Amazon Bedrock metrics, AgentCore Observability and AWS CloudWatch metrics to include monitoring for Token Usage, Cost per Interaction, and Tool Execution Latency to detect performance and cost anomalies. |
| Log and Event Monitoring (SIEM) | For agentic workload, Amazon GuardDuty provides intelligent threat detection that analyzes Amazon Bedrock API calls to detect anomalous or potentially malicious use of the agent or LLMs. |
| Code & Model Risk Scanning | Amazon CodeGuru and Amazon Inspector scans agent code and operational environment for vulnerabilities. These tools can’t assess model weights for risk, however AWS does provide Amazon SageMaker Model Card support that can be used to build Model Risk scanning controls. |
| Adversarial Testing (Red Teaming) & Critic/Grader Model | The evaluation tools of Amazon Bedrock help assess model fitness. Amazon Bedrock supports leading model providers allowing GxP systems to use multiple models for secondary and tertiary validation. |
| Internal Audits | AWS Audit Manager automates the collection of evidence for compliance and audits and AWS CloudTrail provides a streamlined way to review agent actions and facilitate procedural adherence. |
| Corrective Controls | |
| Model & Prompt Rollback | Use AWS CodePipeline and AWS CloudFormation to quickly revert to a previous, known-good version of a model or Prompt Template when a problem is detected. |
| System Fallback | AWS Step Functions can help orchestrate a fallback to a streamlined, more constrained model or a human-only workflow if the primary agent fails. |
| Human-in-the-Loop & Escalation Management | AWS Step Functions, Amazon Simple Notification Service (SNS) and Amazon Bedrock Prompt Flow can orchestrate workflows that can pause and wait for human approval, including dynamic approvals based on low agent confidence scores or detected anomalies. |
| CAPA Process | AWS Systems Manager OpsCenter provides a central place to manage operational issues, which can be used to track the root cause analysis of an agent’s failure. |
| Incident Response Plan | AWS Security Hub and AWS Systems Manager Incident Manager can automate response plans for AI security incidents (for example, major jailbreak and data leakage) and provide a central dashboard to manage them. |
| Disaster Recovery Plan (DRP) | AWS Elastic Disaster Recovery (DRS) and AWS Backup provides tools to replicate and recover the entire AI application stack, including deploying to different AWS Regions. |
Conclusion
Healthcare and life sciences organizations can build GxP-compliant AI agents by adopting a risk-based framework that balances innovation with regulatory requirements. Success requires proper risk classification, scaled controls matching system impact, and understanding the AWS shared responsibility model. AWS provides qualified infrastructure and comprehensive services, while organizations configure appropriate controls, maintain version management, and implement risk mitigation strategies tailored to their validation needs.
We encourage organizations to explore building GxP-compliant AI agents with AWS services. For more information about implementing compliance-aligned AI systems in regulated environments, contact your AWS account team or visit our Healthcare and Life Sciences Solutions page.
About the authors
Pierre de Malliard is a Senior AI/ML Solutions Architect at Amazon Web Services and supports customers in the Healthcare and Life Sciences Industry.
Ian Sutcliffe is a Global Solution Architect with 25+ years of experience in IT, primarily in the Life Sciences Industry. A thought leader in the area of regulated cloud computing, one of his areas of focus is IT operating models and process optimization and automation with the intent of helping customers become Regulated Cloud Natives
Kristin Ambrosini is a Generative AI Specialist at Amazon Web Services. She drives adoption of scalable GenAI solutions across healthcare and life sciences to transform drug discovery and improve patient outcomes. Kristin blends scientific expertise, technical acumen, and business strategy. She holds a Ph.D. in Biological Sciences.
Ben Xavier is a MedTech Specialist with over 25 years of experience in Medical Device R&D. He is a passionate leader focused on modernizing the MedTech industry through technology and best practices to accelerate innovation and improve patient outcomes.
