Maturing a vulnerability management program has always been slow work, usually spanning over months. Pairing AI agents with DefectDojo Pro collapses that timeline to days

This training session covers three ways AI and DefectDojo Pro work together.

First, the built-in MCP server, which gives LLMs like Claude, ChatGPT, and Gemini secure & structured access to your data through a curated set of tools, pre-built reporting prompts, and embedded reference standards.

Second, using an AI agent in a fresh DefectDojo instance to generate custom report templates and build custom dashboards on the fly.

And third, DefectDojo Sensei, where AI is built directly into the platform to auto-remediate vulnerabilities with human-approved pull requests and to threat-model a design before the code is even written.

Join us as we walk through:

• What the Model Context Protocol is and how DefectDojo's MCP server differs from a raw API
• Connecting your AI clients, like Claude Desktop, Claude.ai, ChatGPT, and Gemini, to read & query against findings, products, engagements, tests, and users to the model
• AI where nothing is built in: generating custom report templates and building custom dashboards directly from your findings data
• Sensei auto-remediation: preview-first, human-approved AI fixes that open pull/merge requests across GitHub, GitLab, Bitbucket, and Azure DevOps
• Sensei AI threat modeling: STRIDE threat models, attack paths, and security requirements generated from a design before code exists, with the results pushed back into DefectDojo as findings
• Practical AI use cases and best practices: executive dashboards, risk prioritization, tool-effectiveness assessments, compliance reporting, prompting, filtering large datasets, and keeping your API token secure

Speaker

Tracy Walker

Principal Solutions Architect

DefectDojo