AWS AI Customer Case Study
AI-Powered Cybersecurity Compliance & Threat Intelligence Agent
Revolutions.ai helped Cyber Splunk build a multi-agent cybersecurity platform on Amazon Bedrock to automate compliance assessments, continuously monitor control drift, correlate threat intelligence, and accelerate incident response across client environments.
Customer: Cyber Splunk
Industry: Cybersecurity & Compliance Services
Market: SMB & Mid-Market
Partner: Revolutions.ai
Engagement at a glance
- Multi-agent orchestration using Amazon Bedrock Agents.
- Five specialist agents for compliance, monitoring, threat intelligence, incident response, and evidence.
- Continuous compliance drift detection and automated evidence collection.
- Human approval gates for containment actions and client-facing findings.
Customer context
Scaling cybersecurity and compliance services without scaling analyst headcount
Cyber Splunk provides SOC 2, HIPAA, ISO 27001, NIST, GDPR, HITRUST, cloud security, threat detection, and incident response services. Its analysts were spending significant time manually reviewing controls, collecting evidence, and correlating threat feeds.
Key business challenge
- Compliance assessments took 3–5 days per engagement.
- Analysts manually cross-referenced more than 200 controls across multiple frameworks.
- Threat intelligence was fragmented across 12+ feeds, resulting in a 4–8 hour advisory delay.
- Evidence collection consumed approximately 60% of analyst time.
- Compliance drift was detected only during periodic reviews.
- Manual incident response adaptation resulted in a 4+ hour Mean Time to Contain (MTTC).
Business and technical objectives
- Reduce compliance assessment time to under four hours.
- Detect compliance drift within 15 minutes.
- Automate at least 75% of evidence collection.
- Correlate threat intelligence and generate advisories in under five minutes.
- Reduce incident Mean Time to Contain (MTTC) to under 30 minutes.
- Keep false positives below 2% with human review for high-impact actions.
Partner solution
A supervisor-led multi-agent platform built on Amazon Bedrock
Revolutions.ai designed a serverless, event-driven platform in which a supervisor agent coordinates specialist agents, invokes Lambda-backed tools, validates findings, and produces auditable decision traces.
Compliance Assessment Agent
Maps client environments to SOC 2, HIPAA, ISO 27001, NIST, and GDPR controls and prioritizes gaps.
Compliance Assessment Agent
Tracks client posture, identifies configuration drift, and triggers remediation or escalation.
Threat Intelligence Agent
Aggregates MITRE ATT&CK, NVD, CISA KEV, and commercial feeds and correlates IoCs to client assets.
Incident Response Agent
Adapts containment playbooks for resource isolation, IP blocking, credential rotation, and WAF actions.
Evidence & Reporting Agent
Collects audit artifacts and compiles evidence packages mapped to applicable control requirements.
Supervisor Agent
Coordinates cross-domain workflows, enforces validation rules, and maintains traceable decision histories.
Human-in-the-loop governance
Production containment, client-facing compliance findings, threat severity upgrades, and report finalization require human approval. Confidence thresholds prevent autonomous execution when evidence or model certainty is insufficient.
Why Amazon Bedrock
Amazon Bedrock Agents provided AWS-native multi-agent orchestration, model access, tool calling, grounding, traceability, and secure processing inside the customer’s AWS environment.
Technical architecture
High-level AWS architecture
The platform uses an API-driven entry layer, a Bedrock supervisor-specialist pattern, event-driven workflows, multi-AZ Lambda execution, and a secure evidence and observability layer.
Technology stack
AWS services used
AWS service
How it is used
Amazon Bedrock Agents
Coordinates supervisor and specialist agents and produces auditable action traces.
Amazon Bedrock — Claude Sonnet 4
Primary reasoning model for compliance analysis, threat correlation, and reporting.
Amazon Bedrock — Claude Opus 4
Escalation model for novel threats, zero-day analysis, and conflicting control interpretations.
Amazon Bedrock Knowledge Bases
Stores compliance frameworks, threat intelligence, client baselines, playbooks, and evidence templates.
Amazon Bedrock Guardrails
Applies client data protection, PII controls, and grounding checks.
AWS Security Hub
Aggregates client security findings and AWS best-practice control results.
Amazon GuardDuty
Provides continuous threat detection signals to the Threat Intelligence Agent.
AWS Config
Supplies configuration state, historical change data, and compliance evidence.
Amazon OpenSearch Serverless
Supports semantic and keyword search across compliance and threat-intelligence content.
Amazon S3
Stores evidence and reports with versioning and Object Lock controls.
Amazon DynamoDB
Stores client baselines, compliance scores, state, and decision logs.
AWS Lambda
Executes specialist tools, evidence collectors, response actions, and report generation.
AWS Step Functions
Coordinates long-running agent workflows, approvals, retries, and escalation paths.
Amazon EventBridge
Triggers compliance checks, monitoring jobs, and event-driven workflows.
Amazon SNS
Delivers security alerts, approval requests, and client notifications.
Amazon QuickSight
Provides client-facing compliance dashboards and executive reporting.
AWS KMS, IAM, and CloudTrail
Provide encryption, cross-account access control, and tamper-resistant audit logging.
Outcomes and business impact
Measurable improvements within ten weeks of production deployment
3.5 hrs
Compliance assessment time
<5 min
Threat intelligence correlation
<15 min
Compliance drift detection
22 min
Incident MTTC
<2%
False positive rate
82%
Reduction in evidence effort
120+
Client capacity without added headcount
3.4×
Onboarding throughput
- $1.2M incremental ARR enabled in six months without additional hiring.
- Three active breaches contained with human approval before data exfiltration.
- Zero audit findings challenged across 47 client engagements after deployment.
- Standardized baseline capture improved customer onboarding velocity fourfold.
Security, reliability, and responsible AI
Controls designed for multi-tenant cybersecurity operations
Security and account governance
- Per-client IAM roles with STS temporary credentials and external IDs.
- Isolated S3 prefixes and per-client AWS KMS keys.
- AES-256 encryption at rest and TLS encryption in transit.
- Private service endpoints with no client data exposed over the public internet.
- CloudTrail logging with retention and tamper-protection controls.
Operations and reliability
- Multi-AZ services with a 99.9% availability objective.
- DynamoDB Global Tables for resilience and low RPO.
- CloudWatch dashboards for agent quality, coverage, freshness, and cost.
- Dead-letter queues, retries, and circuit breakers for failed feeds and collection tasks.
- Threat-intelligence pipelines every five minutes and posture checks every 15 minutes.
Responsible AI controls
- Agents cannot issue compliance attestations or final audit sign-off.
- All findings are grounded against indexed client evidence.
- Decision traces link findings to source data and reasoning steps.
- Human approval is mandatory for containment actions and severity escalation.
Partner contribution
- Architecture and agent design.
- Foundation model evaluation and prompt engineering.
- Knowledge base and validation framework implementation.
- Security architecture, DevOps, observability, and knowledge transfer.
Supporting evidence
Audit-ready artifacts maintained for the engagement
Detailed design, configuration, security, operational, and test evidence can be maintained privately for AWS technical validation and customer governance.
Architecture diagram
Bedrock agents, VPC, multi-AZ Lambda, data layer, and security services.
Detailed design document
Requirements mapping, agent design, data flows, non-functional controls, and security.
Configuration document
Environment, IAM roles, service settings, thresholds, and deployment values.
Agent test evidence
Tool-call validation, confidence thresholds, grounding tests, and human approval scenarios.
Security evidence
IAM, AWS KMS, private endpoints, logging, cross-account access, and data-isolation controls.
Operations evidence
CloudWatch dashboards, alarms, DLQs, retry behavior, and incident runbooks.
Responsible AI controls
Guardrails, decision traces, grounding thresholds, denied actions, and approval gates.
Cost model
Model usage, serverless service estimates, storage assumptions, and customer value analysis.
Customer acceptance
Training, handover, acceptance criteria, milestone feedback, and support model.