Agent GRC: AI-Powered Governance, Risk & Compliance on AWS

Executive Summary 

XenonStack developed AgentGRC, Agent GRC is an Agentic Governance Platform delivering Responsible AI, multi-cloud compliance, and autonomous oversight aligned with ISO 42001 and EU AI Act. As enterprises scale AI adoption and cloud workloads, traditional compliance tools fall short in the era of AI regulations and explainability demands. AgentGRC shifts compliance into a continuous, autonomous discipline. Leveraging AWS services such as IAM, Config, CloudWatch, GuardDuty, Macie, and SageMaker, the solution provides year-round audit readiness, AI risk monitoring, and unified regulatory mapping. Organizations achieved continuous compliance, reduced audit fatigue, improved risk transparency, and 40% lower compliance operational costs. 

Customer Challenge 

Customer Information 

• Customer: XenonStack 

• Industry: AI Governance & Compliance Technology 

• Location: Global 

• Company Size: ----- 

Business Challenges 

• Fragmented Regulations: Overlapping standards (SOC 2, HIPAA, GDPR, PCI-DSS, NIST, EU AI Act) created redundant compliance efforts. 

• Reactive Compliance: Teams scrambled during audits to collect CloudTrail logs, IAM mappings, and S3 evidence, leading to “compliance crunches.” 

• AI Risk Exposure: SageMaker/Bedrock workloads faced drift, bias, and explainability risks. 

• Vendor Blind Spots: Third-party SaaS/LLMs lacked oversight, creating compliance gaps. 

• New compliance obligations under EU AI Act and ISO 42001 for fairness and transparency 

• Audit Fatigue: Manual evidence gathering caused errors, delays, and rising costs. 

• Need for automated ethics and bias reporting across all AI models. 

Technical Challenges 

• Legacy GRC Systems: Not designed for AWS-native, real-time monitoring. 

• Integration Complexity: Traditional tools lacked synchronization with AWS Config, GuardDuty, and multi-account setups. 

• Scalability: Compliance pipelines couldn’t scale with multi-region deployments. 

• Evidence Management: Storing, indexing, and retrieving audit artifacts was manual. 

• AI Oversight: Monitoring fairness, lineage, and drift required GPU-enabled resources and new governance models.  

Partner Solution 

Solution Overview 

Agent GRC embeds autonomous governance agents coordinated via LangGraph / A2A framework for continuous oversight across AWS and multi-cloud environments. The solution enables: 

• Continuous Oversight: Agents monitor IAM drift, Config violations, GuardDuty findings, and SageMaker model performance. 

• Unified Regulatory Mapping: “Implement once, satisfy many” by mapping AWS Config rules to multiple frameworks. 

• AI Risk Intelligence: Continuous scoring of drift, bias, anomalies, and lineage in AI workloads. 

• Audit Automation: Evidence stored in Amazon S3 and indexed via DynamoDB/Redshift ensures year-round readiness. 

• AWS-Native Integration: Built on IAM, Config, CloudWatch, Security Hub, Macie, and SageMaker for seamless adoption. 

• Responsible AI Module: It integrates SageMaker Clarify and Bedrock Guardrails for fairness and bias monitoring. 

AWS Services Used 

• Identity & Compliance: IAM, IAM Identity Center, Config, Control Tower 

• Monitoring & Security: CloudWatch, CloudTrail, Security Hub, GuardDuty, Inspector, Macie 

• Data & Storage: S3 (evidence lake), DynamoDB/RDS (metadata), Redshift (dashboards), OpenSearch (indexing), Elasticache (low-latency) 

• AI Oversight: SageMaker, Bedrock 

• Event-Driven Orchestration: Step Functions, EventBridge, Lambda 

• Encryption & Privacy: AWS KMS for evidence encryption 

• ETL & Analytics: Glue for compliance data pipelines 

• Bias and Fairness metrics: AWS SageMaker Clarify  

• Real-time risk dashboards: QuickSight 

• Framework mapping: AWS Audit Manager 

• LLM policy enforcement: Bedrock Guardrails

 Architecture Diagram 

• Core (Agent GRC): Oversight agents, regulatory mapping, risk intelligence, evidence automation, AI governance, orchestration (MCP/LangGraph/A2A). 

• AWS Services: IAM, Config, CloudWatch, Macie, GuardDuty, S3, DynamoDB, Redshift, Elasticache, OpenSearch, Glue. 

• External Systems: ERP/CRM platforms (SAP, Salesforce, Oracle), vendors, LLMs. 

• Dashboards & Auditor Portal: Real-time risk visibility and evidence access. 

Implementation Details 

The implementation followed Agile + DevOps principles: 

• Phase 1 – Discovery (Weeks 1–2): Defined compliance workflows, mapped frameworks, identified AWS services. 

• Phase 2 – Development (Weeks 3–8): Built oversight agents on EKS/EC2, integrated SageMaker drift monitoring, implemented evidence automation in S3/DynamoDB. 

• Phase 3 – Integration & Testing (Weeks 9–12): Connected ERP/CRM and CI/CD pipelines; tested regulatory mapping and dashboards. 

• Phase 4 – Deployment & Monitoring (Weeks 13–16): Multi-region deployment via CloudFormation; enabled auditor portals and real-time dashboards. 

• Phase 5 - Continuous Optimization and Regulatory Update Automation (On-going) – Agent GRC Knowledge Graph fetches new controls quarterly. 

Security & Compliance Considerations 

• IAM + SSO with RBAC 

• KMS encryption for evidence 

• GuardDuty, Inspector, Macie for threat detection & privacy 

• Human-in-the-loop (HIL) workflows for high-risk AI models 

• Responsible AI Controls (ISO 42001 / EU AI Act) – bias logging, human-in-the-loop validation, and model explainability. 

• Explainability reports and bias scores are encrypted via AWS KMS and stored in S3 evidence buckets.