AI + Cyber Red Team Command Center
One command center that correlates AI-native attack paths with conventional cyber findings — not two disconnected tools.
Role: Lead architect — BRD, PRD and full technical blueprint.
The problem
Modern systems blend LLMs, agents and MCP with conventional apps, cloud, containers and Kubernetes — but security tooling still treats “AI security” and “cybersecurity” as two unrelated disciplines producing two unrelated reports. This command center orchestrates both through one policy engine, one evidence model and one attack graph.
Business Context
Security tooling still treats “AI security” and “cybersecurity” as two disciplines producing two disconnected reports — which leaves a real blind spot where an AI-native finding (prompt injection, agent manipulation) and a conventional finding (misconfiguration, exposed service) that combine into one attack path never get connected. Spire Digi Solution needed one command center instead of two point tools with two different audiences.
Architecture
Key capabilities
150+ authorized AI attack scenarios across LLM, RAG, agent, MCP, A2A, multimodal and voice
Pluggable adapters for established tools — Burp Suite, OWASP ZAP, Nmap, Nuclei, Trivy, Semgrep, Gitleaks
Ephemeral, least-privileged, fully-auditable execution workers with a hard kill switch
Cross-domain attack-graph correlation linking AI findings to conventional cyber findings
Durable workflow orchestration for long-running, multi-day assessment campaigns
Executive and technical reporting with remediation tracking and continuous retesting
Security & Risk
AI-native attack paths (prompt injection, agent manipulation, tool abuse) rarely show up in a conventional cyber report, and conventional findings (misconfigurations, exposed services) rarely show up in an AI security report. This command center correlates both into one attack graph so neither blind spot is invisible to the other team.
Outcome
One evidence model and one policy engine instead of two disconnected tools producing two disconnected risk pictures.
Challenges
Integrating 150+ AI attack scenarios alongside established tools like Burp Suite, OWASP ZAP, Nmap, Nuclei, Trivy, Semgrep and Gitleaks meant normalizing very different evidence formats into a single attack graph, while keeping every execution worker ephemeral, least-privileged and auditable with a hard kill switch — so a long-running, multi-day campaign was never left running unattended without a way to stop it.
Lessons Learned
Correlation is the actual product. A pile of findings from AI-specific and conventional tools is only as useful as the single attack graph that connects them — without that correlation layer, both teams keep working from incomplete pictures of the same system.
Architecture pillars
Designed and reviewed against all ten — see how I evaluate every architecture.
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Technology
Want something like this, built properly?
I design and ship systems like this one — from architecture through to production.