AI Security Research

Let AI agents investigate what scanners cannot

Automated scanners catch patterns. Pragma Core's research agents go deeper. They reason about business logic, trace attack chains across multiple files, and produce structured reports that your security team can act on immediately.

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How it works

Autonomous investigation, structured results

Define your research scope. The AI agent reads your code, reasons about security context, and generates a detailed report with findings, attack scenarios, and remediation guidance.

1
Define the scope
Select one or more repositories and describe what you want investigated. Focus on a specific feature, module, or attack surface.
2
Choose investigation depth
Select from multiple depth levels. Quick assessments for triage, or deep investigations that map out entire attack chains across your codebase.
3
Agent investigates autonomously
The AI agent reads source code, reasons about data flow and business logic, identifies vulnerabilities, and constructs attack scenarios without manual prompting.
4
Review and export findings
Browse the structured report in the platform, continue with follow-up questions via chat, or export findings as Markdown or JSON for external reporting.
Capabilities

Security research at the speed of AI

Multi-level depth control
Choose how deep the agent goes. A surface-level review for quick triage, or a thorough investigation that follows data flows, maps trust boundaries, and explores edge cases.
Interactive chat follow-up
After the initial report, continue the conversation with the agent. Ask follow-up questions, request deeper analysis on specific findings, or explore alternative attack paths.
Structured reports
Every investigation produces a structured output with findings, severity assessments, affected code locations, attack scenarios, and actionable remediation steps.
Export to Markdown and JSON
Download research reports in Markdown for sharing with stakeholders, or JSON for integration with ticketing systems and security dashboards.
Multi-repository scope
Research investigations can span multiple repositories. If your application is split across services, the agent analyzes all of them in context to find cross-service vulnerabilities.
Context-aware reasoning
The agent does not just pattern-match. It reads your code, understands your authentication model, follows permission checks, and reasons about whether a vulnerability is actually exploitable in your specific architecture.
Why it matters

Scanners find patterns. Research agents find real vulnerabilities.

Go beyond OWASP Top 10 checks
Business logic flaws, race conditions, and complex authorization bypasses do not appear in pattern-based scanners. Research agents reason about your specific application to find vulnerabilities that require contextual understanding.
Bridge the gap between scans and pentests
Formal pentests happen once or twice a year. AI research fills the gap, providing continuous deep-dive analysis between engagements so your security posture never goes stale.
Scale your security team
Most organizations have more code than their security team can review. AI research agents handle the investigation work, so your engineers can focus on validation, remediation, and architecture decisions.
Build an institutional knowledge base
Every research report stays in the platform. Over time, you build a library of security investigations that new team members can reference and that auditors can review.

Put AI agents to work on your hardest security questions

Define a scope, set the depth, and let autonomous agents investigate your codebase for real vulnerabilities.

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