AURA Incident Commander

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  Multi-signal correlation graph showing alert clustering, timeline evolution, and root-cause indicators.

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  LLM-generated prioritized actions with risk tagging and human-approval safeguards.

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  Unified incident view with correlated AWS alerts, severity scoring, and real-time AI-generated summary.

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  Guarded automation flow using Lambda and Systems Manager with explicit approval control.

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  Real-time status tracking, resource impact metrics, and auditable decision history.

Inspiration

Modern cloud teams don’t fail because they lack monitoring — they fail because they are drowning in signals.

Security and SRE teams receive alerts from CloudWatch, GuardDuty, Security Hub, and other AWS services, but correlation, prioritization, and coordinated response still happen manually across dashboards, chats, and static runbooks.

AURA was built around a simple question:

What if every team had an AI Incident Commander that could think clearly under pressure, correlate AWS signals in real time, and guide responders step-by-step?

What it does

AURA is an AI-powered Incident Commander for AWS environments.

It:

Ingests alerts and logs from AWS services

Correlates related events into structured incidents

Generates concise, context-aware incident summaries

Recommends prioritized next actions

Orchestrates guarded runbooks using AWS Lambda and Systems Manager

Maintains a live, auditable incident timeline

Instead of responders switching between dashboards and guesswork, AURA provides:

Situational awareness → Decision support → Controlled automation

How we built it

AURA follows a layered architecture aligned with AWS best practices:

1. Ingestion & Normalization Layer

Alerts from CloudWatch, EventBridge, GuardDuty, and Security Hub are normalized into a unified incident schema.

1. Enrichment & Context Layer

We enrich signals with metadata such as service ownership, severity mapping, and resource context.

1. AI Analysis Layer

An LLM-driven analysis engine:

Correlates multi-alert incidents

Generates structured summaries

Suggests ranked response actions

Explains reasoning for transparency

1. Orchestration Layer

Safe playbooks are executed via:

AWS Lambda

AWS Systems Manager

Controlled approval gates

1. Web Dashboard

Built with React + Next.js frontend and FastAPI backend, providing:

Real-time incident view

Action approvals

Timeline tracking

Post-incident logs

The architecture separates intelligence from automation to ensure safe, explainable AI-assisted operations.

Challenges we ran into

1. Signal Noise vs. Real Incidents

Multiple alerts from different services often describe the same root cause. Designing correlation logic that avoids duplication while preserving context required careful modeling.

1. AI Consistency

LLMs can produce vague suggestions. We had to:

Structure prompts tightly

Constrain outputs into defined schemas

Ensure recommendations are actionable, not generic

1. Safe Automation Boundaries

We explicitly defined:

What can be auto-executed

What requires human approval

What should remain advisory

This balance was critical to align with responsible AI and security practices.

Accomplishments that we're proud of

Built a working AI-assisted incident response architecture aligned with AWS patterns

Converted “AI copilot” theory into a practical Incident Commander model

Implemented explainable recommendations instead of black-box automation

Designed a modular system that can extend to security, reliability, and generative-AI workloads

Most importantly, AURA reduces cognitive overload during incidents — where clarity matters most.

What we learned

Incident response is fundamentally a context management problem

AI is most valuable when it structures chaos, not when it replaces humans

Automation must be guarded, auditable, and explainable

Clear architectural separation (ingestion → enrichment → AI → orchestration) dramatically improves reliability

We also learned that strong AI systems depend more on structured inputs and constraints than raw model power.

What's next for AURA Incident Commander

Next steps include:

Deeper integration with IAM, asset inventory, and identity context

Agentic multi-step playbooks for complex incidents

Slack / Teams integration for collaboration

Automated post-incident RCA report generation

Support for generative-AI workload monitoring

Adaptive learning from past incidents

Our goal is to evolve AURA into a central incident intelligence layer for AWS environments ​

Built With

• amazon
• amazon-web-services
• cloudwatch
• eventbridge
• fastapi
• guardduty
• hub
• lambda
• manager
• mongodb
• next.js
• python
• react
• security
• systems
• typescript