An Agentic Multi-Agent Architecture for Cybersecurity Risk Management

TL;DR

This paper presents a multi-agent AI system for rapid, cost-effective cybersecurity risk assessment that outperforms traditional methods in accuracy and speed, but faces challenges with context limitations on standard hardware.

Contribution

Introduces a novel multi-agent architecture for cybersecurity risk assessment that integrates sequential analytical stages with shared context, improving efficiency and accuracy.

Findings

Achieved 85% agreement with expert assessments on severity classifications.

Covered 92% of identified risks in real-world healthcare setting.

Failed to complete assessments on standard hardware due to context window limitations.

Abstract

Getting a real cybersecurity risk assessment for a small organization is expensive -- a NIST CSF-aligned engagement runs $15,000 on the low end, takes weeks, and depends on practitioners who are genuinely scarce. Most small companies skip it entirely. We built a six-agent AI system where each agent handles one analytical stage: profiling the organization, mapping assets, analyzing threats, evaluating controls, scoring risks, and generating recommendations. Agents share a persistent context that grows as the assessment proceeds, so later agents build on what earlier ones concluded -- the mechanism that distinguishes this from standard sequential agent pipelines. We tested it on a 15-person HIPAA-covered healthcare company and compared outputs to independent assessments by three CISSP practitioners -- the system agreed with them 85% of the time on severity classifications, covered 92% of identified risks, and finished in under 15 minutes. We then ran 30 repeated single-agent assessments across five synthetic but sector-realistic organizational profiles in healthcare, fintech, manufacturing, retail, and SaaS, comparing a general-purpose Mistral-7B against a domain fine-tuned model. Both completed every run. The fine-tuned model flagged threats the baseline could not see at all: PHI exposure in healthcare, OT/IIoT vulnerabilities in manufacturing, platform-specific risks in retail. The full multi-agent pipeline, however, failed every one of 30 attempts on a Tesla T4 with its 4,096-token default context window -- context capacity, not model quality, turned out to be the binding constraint.