Secure by Design: The Future of Threat Modeling for AI-Native Applications

Modern applications are no longer simple stacks of code and dependencies. They’re often dynamic ecosystems, powered by large language models, autonomous agents, and complex webs of APIs and data flows that shift daily. This transformation has introduced a fundamental challenge: How can we secure systems that are constantly learning, changing, and making decisions autonomously?

The shift from secure code to secure systems

Traditional approaches to security, such as scanning code, running pen tests, or manually reviewing architectures, can’t keep pace. The rise of AI-native applications has created a new class of threats that evolve faster than the manual processes designed to prevent them.

In this new landscape, “secure code” is no longer enough. We need secure-by-design systems architectures that continuously understand, anticipate, and mitigate risk as they evolve in real time. 

Threat modeling is the blueprint of proactive security. It’s how teams understand how their systems work, what could go wrong, and how to prevent it, before attackers ever get the chance. But the traditional process is broken. It’s too slow, too manual, and too disconnected from development reality.

Snyk’s answer is to make threat modeling continuous, intelligent, and connected, using automation, AI, and developer-first workflows to shift this essential practice into the age of agentic software.

Threat modeling struggles to keep up with AI

Organizations already recognize that threat modeling is critical to designing secure systems, yet most struggle to scale it:

• Manual diagrams take weeks; whiteboarding sessions, Visio updates, and documentation all lag behind the increasing pace of development.

• Models drift constantly, code changes daily, but threat models rarely do.

• Generic threat libraries create noise-producing findings that don’t reflect your specific stack or architecture.

• Few experts can maintain threat libraries as knowledge sits in the hands of a few security SMEs, creating bottlenecks.

• Developers aren’t engaged, threat modeling feels like an audit, not an integrated part of building software.

Even the best existing tools, from spreadsheets to specialized platforms, often replicate these problems in digital form. They’re slow, generic, and disconnected from where real development happens. In a world where LLMs, AI agents, and autonomous systems operate dynamically, these limitations make traditional threat modeling obsolete. We need a way to model systems that move as fast as the code, and continuously adapt as architectures evolve.

The evolution of automated threat models, powered by your source code

The Evo by Snyk Threat Modeling Agent represents a new paradigm that is automated, contextual, and continuous. Instead of spending weeks diagramming architecture manually, Evo connects directly to your code repositories (via the Snyk AI-BOM), cloud infrastructure, and runtime data to automatically generate and maintain your system model.

This model updates as your software evolves, detecting when new services are deployed, APIs change, or AI models are added. It eliminates the single biggest pain point in threat modeling: drift. Each model isn’t static documentation; it's more like a living reflection of your system’s architecture and data flows.

It’s powered by your own data, so Evo knows the difference between what’s generic and what’s real. It delivers tailored insights, not templated ones. This helps connect potential threats directly to the data paths and AI components you actually use.

Contextual threats, actionable mitigations

One of the legacy tools' core failures is the gap between threat and action. They tell you what’s wrong in abstract terms such as “potential data exfiltration risk” or “injection vulnerability,” but few tell you what you can do about it.

Each finding from Evo includes:

• Context, where it exists in your system, and what components it affects.

• Actionable mitigations: step-by-step instructions or even pull requests that fix the issue.

• Tailored threat intelligence, leveraging AI to interpret your unique architecture, not a generic checklist.

This turns threat modeling from a theoretical exercise into an engineering workflow and something developers can act on, not just read about. By integrating directly into CI/CD pipelines, issue trackers, and developer tools, threat modeling becomes part of how software is built, not a separate process after the fact.

Evo by Snyk: Built for AI-native applications

AI introduces an entirely new layer of risk, one that traditional tools simply weren’t built to handle.

Evo’s Threat Modeling Agent includes specialized intelligence for AI-native systems, including:

• Mapping data flows and action paths to reveal where agent risk exists as vulnerabilities when autonomous agents take unsafe or unintended actions.) Threat Modeling is the only way to understand and control those actions before attackers exploit them.

• Prompt Injection and Indirect Prompt Injection, where attackers manipulate model behavior or input context.

• Data Exfiltration and Leakage via AI models, revealing internal data or sensitive context through responses.

• Data Poisoning, where malicious data contaminates model training or inference.
  Model Evasion manipulates prompts or inputs to bypass restrictions or filters.

Evo’s AI-Native threat libraries maps these vulnerabilities to real-world mitigations across your stack, from securing MCP clients and servers, to hardening LLM prompts, to validating guardrails and outputs. This specialized approach reflects the reality that AI security goes beyond code; it’s about behavior.

Solving the human bottleneck

Threat modeling has traditionally relied on a small group of experts with deep contextual knowledge. That creates a bottleneck and limits scalability. Evo’s automation reduces the need for specialized expertise to perform the basics. Developers can kick off automated models directly from their own repos, review contextually relevant threats, and even receive pull request-based fixes.

For AppSec teams, Evo centralizes visibility across projects, surfacing critical threats, mapping them to business impact, and enabling faster prioritization.

Security Architects can finally maintain live, accurate models of complex architectures. So you can see not just where risks exist today, but how they evolve with each deployment.

And for emerging AI Security Engineers, Evo provides a dedicated system to model, monitor, and mitigate the unique risks of AI agents, from model-layer manipulation to cross-agent data exposure.

Evo democratizes threat modeling, turning what was once a specialized security ritual into a continuous, collaborative process across teams.

The Evo advantage: Orchestrated threat modeling

Beyond simply automating threat modeling, Evo orchestrates it across the entire security lifecycle, turning a once-static process into a living, adaptive security layer that evolves alongside your AI systems. 

Built on the Evo Agentic Security Orchestration System, threat modeling becomes the connective tissue between discovery, testing, governance, and protection — automatically mapping every model, agent, and dependency through the AI-BOM; continuously validating assumptions with Red Teaming agents; and feeding verified insights into governance and protection workflows. 

This orchestration creates a global context where every system component, from code to runtime, informs the next security decision in real time. When code changes, the threat model updates automatically. When new AI components are deployed, they’re instantly discovered, modeled, and tested. 

Findings feed directly into control validation and policy enforcement, creating a continuous feedback loop that learns and adapts autonomously. The result is more efficiency along with a cultural shift toward secure-by-design development, where security becomes part of how software is built, not a gate at the end. 

Developers gain real-time visibility into threats as they code, while AppSec teams maintain continuous design assurance, a state where every new model, deployment, or agent automatically updates your understanding of risk. While AI systems evolve daily, Evo transforms threat modeling into an intelligent defense layer that scales and strengthens with every change.

Get started with Evo threat modeling

You can start experimenting with Evo’s Threat Modeling capabilities today:

Apply to join the Evo Design Partner Program: Customers can Apply Now to gain early access to the Evo orchestration experience, combining Threat Modeling with Red Teaming, Governance, and Protection in a unified interface.