--- title: "Continuous threat modeling" description: "Clearly AI builds threat models from the Jira tickets, design docs, and code your teams already have, then reruns them every time the system changes or on a schedule you set." canonical: https://clearly-ai.com/solutions/continuous-threat-modeling --- _Continuous threat modeling_ # A current threat model for everything that ships Clearly AI builds threat models from the Jira tickets, design docs, and code your teams already have, then reruns them every time the system changes or on a schedule you set. Each rerun shows what changed since the last approved version, with sources cited and gaps flagged. Your reviewers spend their time on the decisions that need them. - [Contact Enterprise Sales](https://clearly-ai.com/demo) - [Explore threat modeling frameworks](https://clearly-ai.com/solutions/continuous-threat-modeling#frameworks) - HID Global - Ericsson - PayPal - Modern Health - Hagerty - Rivian - Infineon - Bastion ## When did your threat model stop matching the system? A threat model takes days to build. Someone schedules the meeting, draws the architecture, works through the threat categories, and writes it up. By the time it's approved, the design has moved on. A new API ships, an auth flow changes, a dependency gets swapped, and the threat model on file still describes last quarter's system. Most teams can't afford to redo that work for every change, so they don't. Security teams formally review 10–15% of what ships, and the threat models they do have drift further from reality every sprint. AI-generated code is widening that gap. ## How continuous threat modeling works 1. **Start from what you already have** Point Clearly AI at a Jira ticket, design doc, GitHub repo, or intake request. It gathers the context it needs from the enterprise context graph and drafts the first threat model, with findings pre-populated and sources cited. 2. **Rerun on every change or on a schedule** A new ticket, design doc, or pull request can rerun the threat model for the system it touches, and so can a developer's coding agent through the Clearly AI MCP server. Critical systems can also rerun daily, weekly, or quarterly, even when no one opened a ticket. 3. **Compare against the last approved version** Each rerun shows new, changed, and resolved findings since the threat model your team last approved, so reviewers confirm the difference instead of rereading the whole model. 4. **Approve and route** A reviewer on your team confirms the changes and signs. Findings go to Jira, Slack, and the risk registry. [See how workflows trigger and schedule reviews](https://clearly-ai.com/platform/workflows) _Agentic context_ ## Threat models from what you already have Traditional threat modeling tools start with a blank canvas and a meeting. Clearly AI starts with the artifacts your teams already wrote. - **The context is already written down:** Design docs, Jira tickets, wiki pages, architecture diagrams, repos, and infrastructure configs. Clearly AI reads them, so no one has to redraw the system from memory. - **Connected across systems:** The enterprise context graph links each system to the docs, tickets, code, vendors, and past reviews behind it, so every rerun starts from the full picture - **Existing threat models count too:** Upload the threat models your team already has, including exports from other threat modeling tools, and Clearly AI uses them as context - **Gaps flagged:** When a design doc and the code disagree, or a question isn't answered anywhere, Clearly AI marks it as missing or conflicting instead of guessing - **Reviewers step in where judgment matters:** Context gathering and drafting are automated. Your team decides what's real, what's acceptable, and what ships. [See how the enterprise context graph works](https://clearly-ai.com/platform/enterprise-context-graph) _Threat modeling frameworks_ ## Every framework your team works in Clearly AI runs threat models against frameworks like STRIDE, MITRE ATT&CK, and MAESTRO, plus your own methodology. Configure workflows to apply a different framework per system, like MAESTRO for an AI agent and STRIDE for a web service. ### STRIDE Spoofing, tampering, repudiation, information disclosure, denial of service, and elevation of privilege. The most widely used way to find threats component by component in software systems. ### MITRE ATT&CK A knowledge base of real adversary tactics and techniques. Use it to map a system's attack surface to how attackers actually operate. ### MAESTRO A threat modeling framework built for agentic AI, covering the layers of an AI system from foundation models to agent ecosystems. Use it for agents, LLM applications, and multi-agent systems. ### PASTA The Process for Attack Simulation and Threat Analysis. A seven-stage, risk-centric method that ties technical threats to business impact. ### LINDDUN A privacy threat modeling framework covering linking, identifying, non-repudiation, detecting, data disclosure, unawareness, and non-compliance. Use it where personal data is involved, alongside your privacy reviews. ### The Four Question Framework What are we working on? What can go wrong? What are we going to do about it? Did we do a good job? A simple structure that works for any system and any team. ### Your own methodology Upload your threat modeling template, policies, or control set. Clearly AI turns it into a versioned review template in hours. [See how tuning and customization work](https://clearly-ai.com/platform/quality) _Drift detection_ ## Catch drift between what was approved and what was built The design your team signed off on isn't always the system that ships. Clearly AI checks the difference. - **Checked against the approved review:** Implementation reviews compare a change to the design, the ticket, the plan-mode guidance, and the existing threat model - **Blast radius sized first:** When a change affects security, Clearly AI sizes how far it reaches across the system before deciding how deep the review goes - **Updated when it matters:** Security-impacting changes get an updated threat model. Minor changes don't add noise. - **New and resolved findings, side by side:** Reviewers see exactly what moved since the last approved version [See how implementation reviews work in the IDE](https://clearly-ai.com/platform/ide-and-mcp) _SDLC integration_ ## Built into how your teams ship Continuous threat modeling only works if no one has to remember to start it. - **Triggered from your tools:** Jira, GitHub, GitLab, Azure DevOps, Linear, ServiceNow, and Aha! can start a threat model with no manual step. - **In the developer's loop:** Developers and their AI coding agents get security requirements in plan mode and implementation reviews before merge, through the Clearly AI MCP server - **On a schedule you set:** Daily, weekly, or quarterly reruns keep critical systems current between changes - **When a new threat lands (coming soon):** Threat intelligence feeds can trigger targeted re-reviews of past threat models a new attack affects [See Threat intelligence](https://clearly-ai.com/platform/threat-intelligence) [See all integrations](https://clearly-ai.com/integrations) ![Development tools triggering Clearly AI threat models, with findings routed back to the tools security and engineering teams already use.](https://clearly-ai.com/solutions/workflow-diagram-trigger-review-flat.webp) _Triage and prioritization_ ## Work the findings that matter first More coverage means more findings. Clearly AI helps your team work them in the right order. - **Severity with reasoning:** Each finding is rated with the reasoning behind it, using your risk thresholds and severity definitions - **A lighter path for low-risk changes:** Workflows can send changes that don't affect security to a lighter review or resolve them automatically, so reviewers focus on the changes that do - **One queue for every finding:** Findings from every threat model land in the risk registry with owners, status, and history - **High-criticality findings move on their own:** Workflows open Jira tickets or post to Slack, and [Clearly AI verifies the fix landed](https://clearly-ai.com/platform/control-and-remediation-verification) Need to check the whole portfolio against baseline controls first? [See Product security assurance](https://clearly-ai.com/solutions/product-security-assurance) ![Threat model findings in the Clearly AI risk registry, sorted by severity with owners, status, and due dates.](https://clearly-ai.com/risk-registry/figma/registry-screenshot.png) [See how the risk registry works](https://clearly-ai.com/platform/resolve/risk-registry) _Audit-ready accuracy_ ## Why not just ask a general-purpose AI for a threat model? You'll get a draft fast. What you won't get is a threat model your team can sign and an auditor can follow. Clearly AI is built for that part. - **Every finding cites its source:** The document, ticket, or code behind each finding is one click away - **Versioned templates:** The same inputs reviewed against the same template produce the same findings, and each review records which version it ran - **Ask why:** Reviewers can ask the Clearly AI agent why it reached a finding, push back, and rerun - **A recorded decision:** Clearly AI records who approved each threat model, when, and against which project state - **Nothing to maintain:** New frameworks, regulations, and threat patterns come built in, so your team isn't writing and maintaining its own review system [See how Clearly AI keeps findings accurate and traceable](https://clearly-ai.com/platform/quality) ![A Clearly AI threat model finding with the design doc, ticket, and code that support it listed in a Sources panel.](https://clearly-ai.com/solutions/audit-ready-sources-panel-2026-10.webp) ## Coverage that keeps pace with every release - **~90%** Reduction in time per review - **4x** Review capacity with the same headcount - **From 10 to 100%** Product coverage > “Weeks to minutes, literally.” > > — William Brown, Sr. Director of Product Security and Privacy [Read the HID Global story](https://clearly-ai.com/customer-stories/hid-global) _FAQ_ ## Answers to your top questions ### What is continuous threat modeling? Continuous threat modeling keeps a threat model current as a system changes, instead of building it once and filing it. Clearly AI is a continuous threat modeling platform: threat models rerun when a ticket, pull request, or design changes, or on a schedule you set, and each rerun shows what changed since the last approved version. ### Is continuous threat modeling the same as continuous monitoring? No. Clearly AI doesn't watch your systems, networks, or dashboards. Every threat model is a point-in-time review of the system as described in your design docs, tickets, and code, started by a change or a schedule. Continuous means every change gets reviewed. ### How often can Clearly AI rerun a threat model? As often as you need. Reruns can trigger on relevant Jira tickets, pull requests, or work items, and you can also schedule them daily, weekly, quarterly, or on a custom cadence. [See how workflows trigger and schedule reviews](https://clearly-ai.com/platform/workflows) ### Which threat modeling frameworks does Clearly AI support? Frameworks like STRIDE, MITRE ATT&CK, MAESTRO, PASTA, LINDDUN, the Four Question Framework, and OWASP, plus your own custom methodology. You can configure workflows to apply a different framework per system, like MAESTRO for AI agents and STRIDE for web services. ### Can we use our own threat modeling template? Yes. Upload your template, policies, or control set and Clearly AI turns it into a versioned review template in hours. Developers can also manage templates as code in YAML. [See how tuning and customization work](https://clearly-ai.com/platform/quality) ### Can we start from the threat models we already have? Yes. Upload your existing threat models, including exports from other threat modeling tools, and Clearly AI uses them as context for the next review instead of starting over. ### What is threat model drift? Drift is the gap between the system your team reviewed and the system that actually shipped. Clearly AI checks implementation changes against the approved design, ticket, and existing threat model, and updates the threat model for changes that affect security. ### What does Clearly AI need to build a threat model? The artifacts your team already has: design docs, Jira tickets, architecture diagrams, wiki pages, code repositories, and infrastructure configs. Clearly AI gathers that context itself and flags what's missing, so no one has to fill out a blank diagram first. ### Will continuous threat modeling flood my team with findings? No. Workflows can send changes that don't affect security to a lighter review or resolve them automatically, findings are rated against your risk thresholds, and reruns show only what's new or changed since the last approved version. ### Does Clearly AI replace security engineers? No. Clearly AI gathers context and drafts the threat model. A reviewer on your team challenges, edits, and approves it, and Clearly AI records who approved it and when. ### Can developers run threat models from the IDE? Yes. Developers and their AI coding agents can request security requirements in plan mode and run implementation reviews through the Clearly AI MCP server in tools like Cursor, Claude Code, Kiro, Devin, and Codex. [See IDE and MCP](https://clearly-ai.com/platform/ide-and-mcp) ## Learn more - [Secure everything that ships](https://clearly-ai.com) - [We built the tool we wished we had](https://clearly-ai.com/about-us) - [One platform. From intake to remediation.](https://clearly-ai.com/platform) - [Know everything that needs a review. Skip the 300-question form.](https://clearly-ai.com/platform/security-intake) - [Ask only what matters. 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Every risky change threat modeled.](https://clearly-ai.com/platform/ide-and-mcp) - [Verify what actually shipped](https://clearly-ai.com/platform/control-and-remediation-verification) - [A current threat model for everything that ships](https://clearly-ai.com/solutions/continuous-threat-modeling) - [Prove every product that ships meets your security bar](https://clearly-ai.com/solutions/product-security-assurance) - [Privacy reviews your team can stake decisions on](https://clearly-ai.com/solutions/privacy) - [Every vendor reviewed against your standards](https://clearly-ai.com/solutions/third-party-risk) - [Review every design before it's built](https://clearly-ai.com/solutions/design-and-architecture-reviews) - [Secure the AI you build, buy, and code with](https://clearly-ai.com/solutions/ai-agentic-security) - [Every regulation you answer to. 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