AI Threat Modeling
Manual and intermittent by design. It cannot track continuous, AI-driven change, so the model is stale between the review and the release.
Prevention, not paperwork
How AI Threat Modeling works
Threat modeling shifts from a point-in-time workshop to continuous prevention embedded in how software is built. Three outcomes follow.
Deprioritized: internal tool, no sensitive data, controls in place
Guardian Agent
Every change analyzed against your evolving architecture. Threats and countermeasures generated automatically, fed into prompts before code exists.
Diagrams, not architecture
Same input: a ticket to add a customer data export. Opposite risk reality. Both verdicts are possible only with code-to-runtime context in the Software Graph. A diagram-based tool models them the same way and prioritizes neither.
Design flaws caught before code are cheap to fix while the architecture is still flexible. Found in production, they are the costliest risk you carry. AI development has pushed the critical decisions even earlier.
Models built on whiteboards and static representations of systems that no longer stay still. The diagram is wrong the moment the next commit lands.
Why diagram-based tools miss it: they model the box, not the blast radius. Guardian Agent maps the export to a real internet-facing service and a live PII flow, generates the threat story, and feeds the countermeasures into the coding prompt before the export is written.
Disconnected output
Risk born before code
Countermeasures feed straight into AI coding prompts through Secure Prompt. Code from Copilot or Cursor arrives already aligned with the threat model, instead of a document no one reads.
Reduce cost by preventing risk before code exists
Grounded in real architecture
Autonomous monitoring detects new feature tickets in Jira, GitHub, and Azure DevOps and triggers threat analysis with no human in the loop. The model evolves as the architecture evolves.
AI coding agents ship features from idea to production in minutes, and architectures never stop moving. Manual, intermittent threat modeling was built for a world that no longer exists. Four failures follow.
The most critical risks are decided early: data flows, integrations, trust boundaries. Without visibility into those decisions, risk ships before a line is written.
Always on, never stale
Explore the Data Fabric that powers it →
Legacy threat modeling no longer reflects reality
Recommendations land in a report detached from the actual implementation. Developers never see them in the workflow, so the threat model dies as a document.
What AI Threat Modeling delivers
Why a checklist over-flags it: "data export" trips the same rule and demands the same review. The Software Graph proves it's internal, non-sensitive, and already controlled. Guardian Agent deprioritizes it with evidence.
AI Threat Modeling
Can't keep pace
One feature. Two threat models.
Read: introducing AI Threat Modeling →
Five capabilities turn threat modeling from a point-in-time exercise into continuous prevention: automated, architecture-aware, embedded in development.
Legacy threat modeling reads diagrams. Guardian Agent reasons over your code-to-runtime Software Graph like an architect: detects new features on its own, models threats against your architecture, and prevents risk before code exists.
Threats come from actual code, cloud, and runtime, not a whiteboard that drifts from reality. The Software Graph ties design intent to how the system truly behaves.
Read: stay secure by design →