Aidena

— AI STACK RECOMMENDATION

AI Vulnerability Scanner & Patch Recommendation System

Automated security scanning with AI-powered patch recommendations. Combines code analysis, vulnerability detection, and intelligent remediation suggestions for application security at startup scale.

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Security

AI Vulnerability Scanner & Patch Recommendation System

Automated security scanning with AI-powered patch recommendations. Combines code analysis, vulnerability detection, and intelligent remediation suggestions for application security at startup scale.

high confidence

Core Stack ℹ︎

Claude Opus 4

Primary

Most capable model for analyzing code vulnerabilities, understanding security context, and generating accurate patch recommendations with reasoning transparency.

$50-200/month

CrewAI

Primary

Multi-agent orchestration for coordinating scanner agents (code analyzer, vulnerability detector, patch recommender) working together on security workflows.

$0/month

Complete the Stack ℹ︎

Firecrawl

Alternative

Scrapes vulnerability databases and security advisories to feed real-time threat intelligence into patch recommendations.

$0-50/month

Arize Phoenix

Alternative

Open-source observability for tracing vulnerability detection logic, evaluating recommendation quality, and debugging false positives.

$0/month

DeepEval

Alternative

Evaluate patch recommendation accuracy, relevance, and safety before deployment using automated security-focused metrics.

$0/month

Beam Cloud

Alternative

Serverless GPU platform for scaling vulnerability scanning jobs without managing infrastructure, pay-per-second billing ideal for startup costs.

$10-100/month

Getting started

  1. 1Set up CrewAI with three specialized agents: CodeAnalyzer (parses source code for vulnerabilities), VulnerabilityDetector (cross-references against CVE databases), PatchRecommender (uses Claude Opus to suggest fixes).
  2. 2Integrate Firecrawl to continuously ingest latest security advisories and CVE feeds.
  3. 3Deploy scanning pipeline on Beam Cloud for auto-scaling based on scan volume.
  4. 4Use Arize Phoenix to trace each vulnerability detection and patch recommendation through the agent workflow.
  5. 5Implement DeepEval metrics to validate patch quality (correctness, security impact, code style compliance).
  6. 6Create API endpoint exposing scan results with recommended patches ranked by severity and confidence.
  7. 7Set up monitoring dashboard tracking false positive rates and patch acceptance rates.
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