Why AI-Native Apps Break Traditional AppSec Models

AI-native applications don’t behave like the software security teams are used to protecting. Instead of following predictable logic paths, these systems generate code on the fly, reshape user inputs, and make context-dependent decisions at runtime. A model’s behavior is shaped by a mix of training data, fine-tuning, prompts, and retrieval source variables that shift its decisions in ways developers may not anticipate.

This expands the attack surface beyond code and configuration. A prompt can redirect a model’s reasoning, a poisoned dataset can distort future outputs, and a poorly constrained agent can take actions developers never programmed. None of this fits neatly into the static, repeatable patterns that traditional AppSec tools rely on.

The result is a landscape filled with questions legacy scanners cannot answer. Which model is driving a key workflow, and what data shaped it? How does a RAG system behave when its retrieval source changes? These are fundamental security concerns, but traditional tools lack the visibility to answer them. AI-native software has reshaped the problem, and older assumptions no longer hold. 

Traditional AppSec assumes predictable code paths

Traditional AppSec practices assume software follows predictable paths. Static Application Security Testing (SAST) works because code can be traced, and Dynamic Application Security Testing (DAST) works because interfaces behave in repeatable ways. Both approaches rely on the idea that developers authored the logic and that it will behave consistently every time.

AI-native applications break this foundation. An LLM may produce different outputs for similar inputs because its behavior shifts in response to prompts, context, and the information it retrieves. The variability isn’t tied to code changes but to the model’s internal reasoning.

This variability undermines the predictability that traditional scanners rely on. SAST can’t map nondeterministic logic, and DAST can’t replay interactions when outputs shift at runtime. Neither can assess logic generated on the fly. When a model shapes core behavior instead of static code, legacy tools can’t provide reliable assessments.

AI-native threat classes don’t map to old categories

The mismatch becomes even sharper when examining the threat categories themselves. Traditional scanners excel at identifying vulnerabilities in code, but AI-native risks often originate outside of code. They emerge from behavior, placing them outside the reach of most existing tools.

Prompt injection shows this clearly: a single input can redirect a model’s reasoning, bypass guardrails, or reveal internal data without touching the code. Data poisoning introduces a similar risk earlier in the lifecycle, where polluted training data leads to harmful or exploitable outputs long after deployment.

RAG workflows introduce risk because manipulated retrieval sources can influence model behavior without requiring any code changes. The broader model supply chain, including pretrained models, embeddings, and third-party agents, introduces opaque dependencies that teams often adopt without full visibility.

All of these threats emerge at runtime rather than in static code, creating a class of vulnerabilities that traditional scanners can’t detect. The result is a growing set of blind spots that most AppSec programs were never built to handle.

The failure point: AppSec has no model-layer visibility

These emerging threat classes expose a deeper issue: most AppSec programs have almost no visibility into the model layer. Teams can map software dependencies, but they rarely track which models run in production or the data and training processes that shaped them.

Key interactions are also opaque. There’s no standard way to trace prompts, review guardrails, or observe inference-time decisions. Traditional logs capture API calls, not the reasoning steps or agent actions that drive AI behavior.

As a result, traditional scanners can’t spot model drift, unexpected agent actions, or changes driven by external data. They were never built to observe model-layer behavior, making blind spots inevitable without new methods of visibility.

AI-native apps require a new security artifact: the AI Bill of Materials (AI-BoM)

This visibility gap highlights the need for a new type of security artifact in AI-native systems. The AI Bill of Materials (AI-BoM) provides a structured way to document the models in use, the datasets that shaped them, and the prompts or agents that influence their behavior, much like an SBOM clarifies software supply chain components.

Consolidating these elements restores the context that AppSec programs lack. It gives teams a clear view of model origins, data influences, and evolving risk. The AI-BoM also supports governance and compliance by showing how AI systems are constructed and what they rely on. As workflows become more complex, they serve as a consistent anchor for assessing and detecting risk.

The AI-BoM reestablishes the foundation AppSec needs. Without it, teams are forced to react to behavior they can’t see, but with it, AI can fit into the same structured, accountable security framework used for software at scale.

Why agentic behavior is the breaking point

As teams begin documenting models, datasets, and prompts through an AI-BoM, another challenge becomes impossible to ignore: AI systems don’t just generate outputs anymore; they take actions. Autonomous agents can call tools, write files, update systems, and trigger workflows based on goals rather than fixed logic. Their behavior shifts with context and retrieved information, creating a level of autonomy that traditional AppSec was never built to analyze.

Legacy approaches can evaluate functions and control flows, but they can’t reason about intent or map the branching action chains an agent might form at runtime. Those paths change moment to moment, not because the code changes, but because the reasoning behind the actions does.

Securing AI-native applications means tracking not only the code, but the behavior it produces over time. It requires visibility into how agents act, what they interact with, and how their decisions evolve. This dynamic autonomy marks the breaking point for traditional AppSec, making a new security model unavoidable.

What a modern model-aligned security approach needs

Recognizing the limits of legacy AppSec is only the first step. The next step is defining what a security model looks like when it is aligned with how AI systems actually behave. If logic is dynamic and agents can act autonomously, security must shift from analyzing static artifacts to understanding the full lifecycle of model behavior.

That starts with visibility. Teams need a clear view of which models are in use, the data that shaped them, how they behave at inference time, and the actions their agents can initiate. Without this context, every downstream control becomes reactive.

Guardrails must evolve as well. It’s no longer enough to verify code quality or configuration hygiene. Modern guardrails need to evaluate model behavior, trace prompt flows, and detect when outputs diverge from expected patterns. They should be able to flag reasoning that veers into unsafe territory, not just errors embedded in code.

Because these systems are adaptive, continuous monitoring is necessary. Drift, data poisoning, unsafe outputs, and unexpected tool use can appear gradually or all at once. Traditional periodic scanning misses these shifts. AI-native systems require ongoing observation so teams can detect changes the moment they matter.

And finally, all of this must integrate with existing development workflows. If security only enters the picture after deployment, organizations end up retrofitting controls around behavior that has already taken shape. Embedding these capabilities earlier in the lifecycle enables teams to act before risks take root.

A model-aligned security approach doesn’t replace AppSec. It extends it to match how AI systems think, decide, and act.

AppSec evolves into AI-security orchestration

A model-aligned approach creates the foundation, but organizations still need a way to put that approach into practice. This is where application security begins to evolve into something new: orchestration across the entire AI system. Instead of treating models, prompts, datasets, and agents as opaque components, security has to coordinate them, observe their behavior, and apply guardrails as they operate.

Evo represents this shift. It extends security beyond code and configuration by orchestrating specialized agents that monitor how models behave, detect threats at the model layer, and apply guardrails in real-time. These agents provide teams with a means to understand and control the components of AI systems that traditional AppSec can’t see, the reasoning, retrieval choices, and actions taken by autonomous workflows.

Snyk Studio complements this at the other end of the lifecycle. It guides AI coding assistants during code generation, ensuring that AI-written code starts secure rather than requiring fixes later. Studio anchors “secure at inception,” while Evo anchors secure behavior as models run, adapt, and act.

Together, they broaden the definition of application security. In an AI-native world, securing an application means securing the code that runs it, the models that drive it, and the agents that act on its behalf. Evo brings these elements together. It shifts AppSec toward a future built on orchestration rather than static analysis.

Traditional AppSec isn’t wrong — it’s incomplete

Traditional AppSec hasn’t failed. The environment around it has changed. AI-native software breaks long-held assumptions about how logic is written, how systems behave, and where risk emerges. Code is no longer the sole source of truth. Behavior, context, and model-driven decision-making shape applications in ways static tools were never designed to interpret.

Securing this new class of systems requires continuous visibility into how models reason, how agents act, and how workflows evolve. The organizations that recognize this shift early will avoid blind spots that slow development and expose them to hidden risk. They’ll move faster, build with more confidence, and treat AI as an accelerant rather than an uncontrolled variable.

Snyk is helping lead that change. By extending security from code to models, prompts, and agentic behavior, we’re moving AppSec from a code-centric discipline to an AI-aligned one. That is the direction the industry must take, and it’s the direction we’re building toward.

Looking to start building AI-native applications with guardrails built in? Unlock the ultimate guide to securing agentic apps and navigating the new threat landscape.