AI Security Operations Monitor
Watch logs and cloud activity for threats, triage alerts, and explain what to do.
- Project range
- $9,000–20,000
- AWS running cost
- $100–450/mo
- Time to deploy
- 4–7 weeks
- Best-fit industries
- SaaS, Financial services
Executive summary
A security-operations layer that aggregates findings and logs from across your cloud, correlates them into prioritized incidents, and uses AI to triage, explain, and recommend response steps. It gives small teams the effect of a security analyst on watch — cutting through alert noise, surfacing what matters, and guiding action without a full SOC.
Business problem
Security signals are scattered across services and drowned in noise. Small teams can't monitor everything, real threats hide among false positives, and when something does fire, staff without deep security expertise struggle to interpret it or respond quickly.
Architecture
AWS services
Amazon GuardDuty
Security- — Threat detection across accounts and workloads
- — Managed finding generation
AWS CloudTrail
Security- — Record of API and account activity
- — Source for behavioral correlation
AWS Lambda
Compute- — Correlate findings into incidents and triage
- — Manage incident state and response
Amazon Bedrock
AI / ML- — Explain findings in plain language
- — Recommend prioritized response steps
Correlation rules
Compute- — Group related signals and assign severity
- — Suppress known false positives
Amazon DynamoDB
Database- — Incident records and response state
Amazon S3
Storage- — Immutable evidence and incident history
Amazon SNS
Messaging- — Alert on-call and escalate incidents
Amazon CloudWatch
Observability- — Operational logs, metrics, and alarms
Data flow
- 1
GuardDuty findings, CloudTrail events, and logs flow to EventBridge in near real time.
- 2
Lambda correlates related signals into incidents, assigns severity, and suppresses known false positives.
- 3
Bedrock explains each incident in plain language and recommends prioritized response steps.
- 4
Incidents and evidence are tracked in DynamoDB and preserved immutably in S3 for audit.
- 5
SNS alerts on-call and escalates via ITSM; a dashboard shows the current security posture.
Security considerations
- Read-focused monitoring; response actions gated behind human approval.
- Immutable evidence store supports incident forensics and audit.
- Least-privilege IAM with segregation between detection and response.
- Findings cite the underlying signals for verifiability.
Cost considerations
- GuardDuty and Bedrock triage are the main variable costs.
- AI reasoning is applied to correlated incidents, not raw event volume.
- S3 lifecycle tiering keeps evidence retention affordable.
Scalability
- Serverless and event-driven; scales across accounts and regions.
- New signal sources and rules extend coverage without rearchitecting.
- Severity and suppression policies configurable per environment.
Deployment roadmap
Phase 1 — Sources & severity
Weeks 1–2- — Enable detection sources and define severity/routing
- — Provision AWS foundation and evidence store
Phase 2 — Build & correlate
Weeks 3–5- — Build correlation, triage, and alerting
- — Tune suppression against historical noise
Phase 3 — Operationalize
Weeks 6–7- — Stand up on-call routing and dashboard
- — Validate playbooks and response guidance
Future enhancements
- Guided or automated containment for well-understood threats.
- Threat-intelligence enrichment of findings.
- User and entity behavior analytics.
- Security posture trends into the executive dashboard.