A leading enterprise sought to proactively test the resilience of its infrastructure and AI models against evolving adversarial threats. Traditional penetration testing and manual red-teaming were insufficient to keep pace with emerging risks such as prompt injection, data poisoning, and supply-chain exploitation. By deploying Agent Red Teaming on AWS, the customer achieved continuous adversarial simulation, automated SOC validation, and compliance-ready evidence generation. The solution improved mean time to detect/respond (MTTD/MTTR) by 40%, reduced false positives by 30%, and provided audit-ready evidence to meet regulatory obligations.
Customer: [Confidential – Global Enterprise]
Industry: Financial Services (can be adapted)
Location: [Primary Location]
Company Size: ~10,000 employees
Growing cyber adversary sophistication with AI-driven phishing and exploit automation.
Existing red-teaming processes were manual, time-bound, and lacked repeatability.
SOC faced alert fatigue with limited ability to validate detection coverage.
AI models used in customer-facing services lacked testing against prompt injection or data exfiltration risks.
Compliance teams required immutable, replayable evidence for audits (ISO 27001, SOC 2, GDPR).
The need to reduce dwell time and improve response readiness under critical business pressure.
Legacy SOC infrastructure not integrated with modern adversarial testing.
Lack of automation in red-team testing cycles.
Difficulty scaling scenarios across multiple business units.
Data pipelines vulnerable to schema drift and poisoning.
Credential hygiene issues (default creds, exposed API keys) identified in early reconnaissance.
Integration requirements with AWS-native services (Security Hub, GuardDuty, IAM).
Agent Red Teaming was deployed as an AWS-native adversarial simulation platform. The solution orchestrates specialized AI-powered Red Team agents — ReconSentry, ExploitScout, PhishCrafter, PayloadMutator, EvasionSim, AlertSpoofer, TTPComposer, DataPoison — with governance provided by PolicySentinel and compliance outputs from EvidencePackager.
The platform continuously tests infrastructure and ML endpoints, integrates with AWS Security Hub and GuardDuty, and delivers actionable remediation insights and compliance packs.
Amazon EKS: Runs orchestrator and agent pods.
Amazon S3: Stores evidence packs, artifacts, and replay data.
AWS Secrets Manager: Manages ephemeral credentials securely.
Amazon DynamoDB: Stores metadata and agent run state.
Amazon CloudWatch: Collects telemetry and metrics from agent runs.
Amazon Kinesis Firehose: Streams telemetry to OpenSearch for replay.
Amazon OpenSearch: Provides indexed attack replay and forensic search.
AWS Security Hub: Consolidates adversarial findings for SOC visibility.
Amazon GuardDuty & Detective: Provide anomaly detection and investigation support.
AWS IAM: Enforces least-privilege access and temporary run roles.
Implemented using Agile methodology, with two-week sprints delivering incremental SOC integrations.
Deployed orchestrator and agents on Amazon EKS in a dedicated red-team VPC.
Integrated with AWS Security Hub and GuardDuty for SOC visibility.
Adopted AWS Secrets Manager to eliminate static credentials and ensure rotation.
Telemetry sidecars streamed logs to CloudWatch and Kinesis; indexed in OpenSearch for replay.
EvidencePackager produced immutable compliance bundles stored in S3.
Security guardrails enforced HITL approvals via PolicySentinel for any live production testing.
Testing strategy included sandbox simulations, controlled live runs, and purple-team exercises.
Implementation timeline: 10 weeks (Phase 1: Recon & Setup; Phase 2: SOC Integration; Phase 3: Full Adversarial Campaigns).
Applied AWS Well-Architected Framework (security, reliability, cost optimization).
Built fully containerized microservices with EKS autoscaling for burst loads.
Leveraged immutable evidence packs in S3 for compliance (GDPR, ISO).
Integrated DevSecOps pipelines: CI/CD triggers red-team tests on model deployments.
Adopted agent-to-agent (A2A) orchestration to emulate realistic adversarial campaigns.
40% reduction in MTTD (mean time to detect).
30% reduction in SOC false positives through AlertSpoofer testing.
50% faster compliance reporting with EvidencePackager outputs.
New capabilities for AI-specific risk validation (prompt injection, poisoning).
Enhanced regulatory alignment with ISO 27001, SOC 2, GDPR, EU AI Act.
Improved SOC confidence and operational readiness.
Scalable EKS deployment capable of running parallel adversarial campaigns.
Automated credential rotation with AWS Secrets Manager.
Replayable telemetry with OpenSearch improved forensic capabilities.
Stronger security posture through proactive attack-path validation.
Reduced technical debt in SOC playbooks via automated remediation updates.
"Agent Red Teaming on AWS transformed our security assurance. We now run continuous adversarial simulations, uncover gaps before attackers do, and generate compliance reports instantly. This has significantly improved our SOC confidence and reduced audit overhead."
— [Customer Name], [CISO / Security Leader]
Integration hurdles with legacy SOC tools addressed by custom connectors into Security Hub.
Credential management shifted from static configs to Secrets Manager.
Data pipeline vulnerabilities mitigated via DataPoison testing and schema validation.
Use sandbox-first, HITL-gated approach to balance realism and safety.
Integrate adversarial testing into CI/CD to catch risks early.
Automate compliance evidence generation for audit readiness.
Expand use of Amazon SageMaker for AI-driven payload generation.
Extend Purple Team simulations with deeper integrations to AWS Shield Advanced.
Continuous partnership to add new adversarial techniques as MITRE ATT&CK evolves.
Long-term plan to offer Red Teaming Agents as a managed AWS Marketplace SaaS.