10x
more threat models created in a large enterprise deployment
ThreatModeler Platform &
GLASSWING
AI speed plus architecture-aware secure design.
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Remediate securely with context
Project Glasswing accelerates vulnerability discovery and remediation. ThreatModeler ensures patches are grounded in system intent, trust boundaries, and architectural context.
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Improve the quality and speed of fixes
AI is helping businesses generate code faster. ThreatModeler captures architecture and intent, and recommends countermeasures, so teams and agents can fix security flaws faster. All powered by the industry's most robust threat framework.
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Create a secure-by-design system that scales with AI
ThreatModeler scales threat modeling across the enterprise with deterministic threat mapping, AI and process governance, and a system of record, enabling you to continuously identify and reduce risk across apps, cloud, and agents.
Everything AI promises for AppSec — plus the secure design context needed to make it count.
Project Glasswing represents an important step forward for AI-assisted vulnerability discovery and remediation. ThreatModeler addresses the design-time side of the same problem: helping teams understand system intent, model threats in architectural context, place controls earlier, and create a governed system of record for secure-by-design decisions across the SDLC.
Together, ThreatModeler and Project Glasswing tell a more complete AppSec story: use AI to find and fix issues faster, and use threat modeling to make those fixes smarter, more relevant, and more aligned to the architecture you are actually trying to secure.
And with MCP, ThreatModeler can bring governed threat modeling context into AI-driven workflows and LLM experiences—so teams are not just remediating faster, but operating with stronger architectural understanding from the start.
Together, ThreatModeler and Project Glasswing tell a more complete AppSec story: use AI to find and fix issues faster, and use threat modeling to make those fixes smarter, more relevant, and more aligned to the architecture you are actually trying to secure.
And with MCP, ThreatModeler can bring governed threat modeling context into AI-driven workflows and LLM experiences—so teams are not just remediating faster, but operating with stronger architectural understanding from the start.
AI is accelerating coding, not understanding
AI is reshaping application security.
Project Glasswing points to a future where vulnerability discovery, exploit, triage, and remediation can move faster than ever. That is a meaningful step forward—especially in large and complex software environments where the cost of finding and fixing issues has historically been high.
But a faster loop doesn’t create more secure code on its own. Without secure context, you risk building a token burning machine that is constantly fixing bugs, and ignoring flaws.
To find and fix architectural flaws, teams and agents need to understand:
Project Glasswing points to a future where vulnerability discovery, exploit, triage, and remediation can move faster than ever. That is a meaningful step forward—especially in large and complex software environments where the cost of finding and fixing issues has historically been high.
But a faster loop doesn’t create more secure code on its own. Without secure context, you risk building a token burning machine that is constantly fixing bugs, and ignoring flaws.
To find and fix architectural flaws, teams and agents need to understand:
- What the system is supposed to do
- Where trust boundaries exist
- Which risks actually matter in context
- How controls should be applied across the architecture
Faster remediation is most valuable when it is guided by that understanding.
Combining speed with understanding
Project Glasswing and the Mythos frontier model can help security teams find vulnerabilities faster. But as we’ve already seen, many vulnerabilities it is finding were already mitigated with security controls and countermeasures. Without the full picture, you’re left patching code that posed no real threat, and introducing more risk, not less.
ThreatModeler helps teams understand architecture and intent so those fixes can be prioritized, better informed, and connected to a broader secure-by-design practice.
ThreatModeler helps teams understand architecture and intent so those fixes can be prioritized, better informed, and connected to a broader secure-by-design practice.
AI gets stronger when threat modeling gives it context
The real opportunity is not choosing between AI and threat modeling. It is combining them.
ThreatModeler brings governed, architecture-aware context into AI-driven workflows so teams don’t just fix issues faster—they fix the right ones, the right way.
ThreatModeler brings governed, architecture-aware context into AI-driven workflows so teams don’t just fix issues faster—they fix the right ones, the right way.
- Architectural intent — understand what the system is supposed to do
- Trust boundaries — identify where risk actually exists
- Control logic — apply protections in the right places
- Reusable decisions — standardize security across systems
Result: better AI output, stronger prioritization, less wasted remediation.
ThreatModeler operationalizes this with AI inside a deterministic framework, so security decisions are consistent, repeatable, and governed across the SDLC.
ThreatModeler + Glasswing
Different roles, stronger together
| ThreatModeler | Glasswing | |
|---|---|---|
| Primary contribution | Operationalizes threat modeling across the SDLC for an enterprise-wide view of risk and a control plane to manage it | Accelerates vulnerability discovery and exploitation |
| Starting point | Architecture diagrams, documentation, IaC, cloud context, system intent | Existing software, code, and systems to analyze and secure |
| Best used for | Understanding what could go wrong, documenting, and mitigating —starting with design and continuing across the SDLC | Finding and exploiting vulnerable code in production |
| Core value | Faster risk reduction with a secure-by-design system of record, design-flaw identification, governance and policy enforcement, and enterprise readiness | Expert vulnerability detection and exploitation |
| System context | Architecture-aware, trust-boundary-aware, secure-design system | Inferred and non-deterministic context |
| Workflow role | Architecture & Design, continuing across the entire SDLC | Downstream vulnerability and remediation acceleration layer |
Bottom line: Project Glasswing and ThreatModeler solve different parts of the same security problem. One accelerates remediation. The other helps ensure remediation is grounded in architectural understanding and secure-by-design discipline.
Speed without context is the new risk
Project Glasswing highlights a real shift in AppSec: AI can help compress the time between discovery and remediation.
That is valuable. But speed alone is not enough.
That is valuable. But speed alone is not enough.
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Enterprise systems need architectural context
When systems span cloud services, APIs, agents, data stores, identities, and infrastructure, teams need to understand how the system is supposed to work before they can decide which findings matter most.
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Not every flaw has the same risk in context
A vulnerability may be technically real but not equally meaningful across every architecture. We’ve already seen this with a large number of Mythos findings. Threat modeling helps teams evaluate findings in the context of trust boundaries, attacker paths, exposure, compensating controls, and business impact.
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Secure design improves remediation quality
When teams already know the intended design, the trust model, and the relevant control strategy, they can fix issues in ways that strengthen the system instead of just closing isolated findings.
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Faster patching needs context
AI can reduce the effort needed to generate fixes, but without architecture-aware context, it cannot choose which fix to implement, or if it should at all.
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Secure-by-design creates a stronger long-term operating model
The goal is not just to remediate faster. It is to continuously reduce preventable risk by improving architecture, standardizing security decisions, and creating a repeatable system of record.
Where ThreatModeler improves outcomes with AI
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ThreatModeler starts with architecture and intent
ThreatModeler captures how a system is designed, not just what code exists. That lets teams and agents identify threats, attacker paths, trust boundaries, and control gaps earlier, when they are cheaper and easier to address.
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ThreatModeler improves the quality of downstream remediation
When vulnerabilities are discovered, teams and agents can use ThreatModeler’s architectural context to understand which findings matter most, how to fix them in line with intended design, and where broader control improvements may be needed.
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ThreatModeler operationalizes secure by design
Threat modeling is how teams and agents translate architecture into security decisions. ThreatModeler turns that discipline into a scalable operating practice across the SDLC with workflow integrations, automation, reporting, and governance.
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ThreatModeler combines AI with a deterministic framework
Prompt-based AI is fast, but variable. ThreatModeler uses AI inside a deterministic threat modeling framework so outputs are structured, reusable, reviewable, and repeatable.
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ThreatModeler creates a governed system of record
ThreatModeler maintains the security ledger: the persistent record of architecture, threats, controls, decisions, updates, ownership, and rationale over time. This becomes your most valuable asset.
Get the ThreatModeler advantage with MCP
The rise of MCP changes the conversation.
ThreatModeler’s Model Context Protocol approach brings governed, deterministic threat intelligence into the AI tools teams already use. That means threat modeling can live inside AI-driven development workflows and can inform any LLM-based experience—including workflows that may eventually include tools like Glasswing.
So the decision is not “LLM or threat modeling.” The better model is:
Use AI wherever it adds speed. Use ThreatModeler to provide the architecture, governance, and deterministic threat context AI alone does not provide.
With MCP, teams can bring ThreatModeler into AI-native workflows so they are not just finding vulnerabilities faster—they are designing more secure systems from the start.
With the ThreatModeler platform, you get:
50%
reduction in effort
5x
faster model creation
2900+
components
100+
protocols
ThreatModeler helps enterprises move from manual, prompt-driven, and checklist-based approaches to governed, architecture-aware threat modeling at scale. Welcome to stronger alignment to compliance, workflow integration, and repeatable reporting.
ThreatModeler vs. Glasswing FAQ
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No. It is valuable. It appears to be a major advance in AI-assisted vulnerability discovery. The point is that vulnerability discovery is not the same category as threat modeling. Fixing bugs, is not the same as avoiding or remediating architectural flaws.
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No, both ThreatModeler and Project Glasswing have their place. And in fact, both become stronger when combined. Still, it is important to understand where vulnerability detection and threat modeling differ.
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Manual threat modeling is a slow process. ThreatModeler has solved that with an enterprise platform built to scale across the enterprise and keep pace of AI. The ThreatModeler platform operationalizes threat modeling with AI, automation, templates, integrations, and a curated threat library, so teams can move faster without sacrificing consistency. So you get a single enterprise-wide view of risk across apps, cloud, and agents and the control-plane to manage it.
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The first myth is that Project Glasswing is reducing remediation costs. In-fact, software architects who have examined the Mythos findings have noted that many of the spotted defects were already mitigated by other security controls. Without proper security context, AI will find, fix, and likely create new vulnerabilities in an endless loop.
You can reduce some of the waste with repeated human review, but the human in the loop will need the same security and architectural context that the agent should have had in the first place. And that’s why vulnerability scanning and remediation alone, is not enough. -
Yes. The ThreatModeler Platform built on an MCP agentic architecture. Our platform is designed to combine governed AI (BYOAI), with the industry’s most robust deterministic threat library. Available through our SaaS application, API endpoints, developer tools integrations, or MCP agentic workflows — ThreatModeler is designed to fit directly into your AI tools and AI-driven SDLC workflows.
The Mythos countdown has started. Prepare with ThreatModeler today.
Whether you have access to Project Glasswing, or you’re stuck waiting in line, there’s work to do right now. Schedule time with our product experts and see how ThreatModeler customers scale to thousands of threat models per year, for an enterprise-wide view of risk, and a single control-plane to manage it.
