The AI security market is growing faster than the shared vocabulary to describe it. Security teams evaluating AI security solutions in 2026 are comparing products that do fundamentally different things under the same label. One vendor's "AI security platform" is an input filter sitting in front of an LLM. Another is a red teaming engine that runs structured attack batteries against deployed models. A third does runtime behavioral monitoring. All three call themselves AI security solutions. None of them is wrong, and none of them is complete on its own.

This guide is written for security engineers and technical security leads who need to cut through that ambiguity: what a complete AI security solution actually covers, how to identify coverage gaps in what vendors offer, and why the three-layer model is the right framework for evaluating anything in this space.

Why AI security is a separate category

Traditional application security was built around a well-understood attack surface: code vulnerabilities, network exposure, authentication flaws, and data in transit or at rest. The controls that address those threats (WAFs, SIEMs, endpoint agents, vulnerability scanners) were not designed to address ML model attack surfaces, because those attack surfaces did not exist when most of those tools were built.

AI systems introduce three attack vectors that have no equivalent in traditional AppSec:

The model itself encodes learned patterns in its weights. Those weights can be extracted through repeated inference queries (model theft), probed to reveal training data (membership inference), or manipulated to trigger specific behaviors under attacker-controlled conditions (backdoor attacks). The model file is an asset with security properties, not just a software artifact.

The training pipeline is an attack surface that standard security tooling does not monitor. Data poisoning attacks inject manipulated examples into training data to corrupt model behavior in targeted ways. Research published in IEEE Security and Privacy demonstrated that poisoning as little as 3% of a training dataset can reliably shift model behavior on specific input classes while preserving overall benchmark performance, making the attack difficult to detect post-deployment.

The inference environment receives untrusted external inputs and produces outputs that downstream systems act on. Prompt injection attacks are the clearest example: inputs engineered to override intended model behavior, exfiltrate context, or hijack agentic workflows. As models take on more autonomous roles in production systems, the blast radius of a successful inference-time attack increases accordingly.

MITRE ATLAS maps this attack surface comprehensively, providing the closest equivalent to MITRE ATT&CK for ML systems. The OWASP LLM Top 10 documents the most operationally significant vulnerability classes. Both are starting points for threat modeling any AI deployment, and both treat AI security as a discipline with its own taxonomy rather than a subset of traditional AppSec.

The three layers a complete AI security solution must cover

Thinking about AI security as a single product category obscures a multi-layer problem. The three layers are distinct in the threats they address, the point in the model lifecycle where they operate, and the controls they require.

Layer 1: Pre-deployment validation

Before a model reaches production, it needs to be evaluated against a structured attack battery. This layer covers supply chain risk (malicious model files, compromised weights from model repositories, serialization exploits in .pt and .pkl formats), backdoor detection, and behavioral validation against known attack classes.

The OWASP Machine Learning Security Top 10 lists supply chain vulnerabilities as a top-tier risk, and the attack surface has grown significantly as open-source model adoption has scaled. Organizations that download community fine-tunes or distilled variants of frontier models inherit whatever properties those models were trained to have, including ones that are not documented in model cards. Repello's research on safety in models derived from DeepSeek-R1 illustrates how distillation can preserve capability while degrading alignment properties, sometimes by design and sometimes as a side effect of the fine-tuning process.

Pre-deployment validation is the layer that most point solutions skip. Input filtering at the API level does nothing for a backdoored model that has already been deployed.

Layer 2: Continuous red teaming

Once a model is in production, its attack surface changes over time. Model updates, prompt template modifications, tool integrations in agentic configurations, and retrieval pipeline changes can all reopen attack paths that were previously evaluated as closed. Point-in-time assessments do not catch these regressions.

Continuous red teaming runs structured attack batteries against deployed AI systems on an ongoing basis, covering adversarial input classes, prompt injection and jailbreak techniques, data exfiltration probes, and behavioral edge cases that manual testing misses. The goal is not compliance box-checking; it is identifying exploitable weaknesses before an adversary does.

Repello's ARTEMIS automated red teaming engine operates at this layer. It runs continuous attack batteries across the attack classes documented in MITRE ATLAS and the OWASP LLM Top 10, adapting coverage as the threat landscape evolves. The research underlying its detection of RAG pipeline poisoning attacks is an example of what continuous red teaming surfaces that static assessments miss: real exploitable weaknesses in retrieval-augmented generation pipelines that affect production AI systems processing untrusted data.

Layer 3: Runtime monitoring

Even with rigorous pre-deployment validation and continuous red teaming, production AI systems will be targeted by attacks that were not anticipated during testing. Runtime monitoring addresses this by detecting and blocking attack patterns at the inference layer in real time, before they reach the model and produce harmful outputs.

Runtime monitoring needs to handle attack patterns regardless of their source: human adversaries crafting prompts manually, automated attack tooling iterating payloads at machine speed, or dark AI tools running unconstrained models to generate jailbreak attempts. The detection challenge is more complex than traditional signature matching because AI attacks are polymorphic by nature. Effective runtime monitoring uses behavioral analysis rather than pattern matching alone.

ARGUS, Repello's runtime security layer, operates at this layer. It monitors production AI systems for attack patterns and blocks them before the model processes them, providing coverage for active exploitation attempts that would otherwise go undetected until an incident is identified downstream.

What most AI security solutions actually cover

The majority of point solutions in the current market address Layer 3 only, and specifically a narrow subset of Layer 3: input filtering for known jailbreak patterns and policy violation detection.

This is not without value. Input filtering blocks commodity attacks and provides a measurable baseline. But it leaves two entire layers of the attack surface unaddressed. A deployment protected only at the input filtering layer is fully exposed to:

• Supply chain attacks delivered via model files before deployment

• Backdoor behaviors embedded during training that activate on specific trigger patterns

• Model extraction through repeated inference queries that do not trigger any content filter

• Membership inference attacks probing whether specific training data points are recoverable

• RAG pipeline poisoning that influences model outputs without touching the inference layer directly

The NIST AI Risk Management Framework provides a governance structure for identifying and managing AI risk across the full model lifecycle. Its emphasis on mapping, measuring, and managing AI risk throughout the development and deployment process reflects the same three-layer structure: you cannot manage risks at layers you are not monitoring.

What to look for when evaluating AI security solutions

The most useful first question when evaluating any AI security vendor is: which of the three layers does this product cover?

For pre-deployment validation: Does the vendor scan model files for known malicious serialization patterns? Does it evaluate model behavior against backdoor detection techniques? Can it assess safety properties in third-party and open-source models before you deploy them?

For continuous red teaming: Does the vendor run ongoing attack batteries, or only point-in-time assessments? Does coverage update as new attack classes are documented? Does it cover the attack classes in MITRE ATLAS and the OWASP LLM Top 10 comprehensively, or a subset? Does it handle agentic architectures and tool-call hijacking, not just single-turn model interactions?

For runtime monitoring: Does the vendor detect behavioral anomalies or only known signatures? How does it handle polymorphic attacks that avoid pattern-match detection? What is the latency impact in production? Does it cover prompt injection in agentic contexts, where the blast radius of a successful attack is significantly higher than in a standard chatbot interaction?

A secondary question: does the vendor provide visibility into what AI is running in your environment before you can secure it? The same governance problem that creates shadow IT risk also applies to AI deployments. Employees running local uncensored models, connecting personal AI tools to corporate systems, or using unvetted AI agents without authorization create an attack surface that pre-deployment validation and runtime monitoring cannot cover if those deployments are invisible. Repello's AI Asset Inventory is built around this problem: before you can secure AI in your environment, you need to know what is actually there.

Frequently asked questions

What is an AI security solution?

An AI security solution is a product or platform that addresses the attack surfaces specific to AI and machine learning systems, including adversarial inputs, model theft, data poisoning, backdoor attacks, and supply chain compromise. These attack surfaces are distinct from traditional application security concerns and require controls that AppSec tooling was not designed to provide. The term covers a range of products from input filters to automated red teaming engines to runtime monitoring platforms, and the capabilities vary significantly across vendors.

How is an AI security solution different from traditional application security?

Traditional AppSec tools address code vulnerabilities, network exposure, and data access controls. AI security solutions address threats that have no equivalent in traditional AppSec: poisoning training data to shift model behavior, extracting model weights through inference queries, inferring whether specific data points were used in training, and triggering hidden backdoor behaviors embedded during training. Most WAFs, SIEMs, and endpoint agents do not detect or mitigate any of these attack classes.

What does a complete AI security solution need to cover?

A complete AI security solution requires three layers: pre-deployment validation (scanning model files, evaluating behavior against known attack classes before deployment), continuous red teaming (ongoing attack testing against deployed models to catch regressions and new attack classes), and runtime monitoring (detecting and blocking active exploitation attempts in production). Most point solutions address only one of these layers. Understanding which layers a vendor covers is the most important question in any evaluation.

What should I look for when evaluating AI security vendors?

Start by determining which of the three layers the vendor covers: pre-deployment, continuous red teaming, or runtime monitoring. Then assess depth within each layer: does red teaming cover agentic architectures and tool-call hijacking, or only single-turn interactions? Does runtime monitoring use behavioral analysis or only signature matching? Does the vendor provide AI asset visibility so you can inventory what AI is running before you attempt to secure it?

Do I need both red teaming and runtime monitoring?

Yes, because they address different threat classes and operate at different points in the model lifecycle. Red teaming identifies exploitable weaknesses before they are actively exploited, allowing you to close attack paths proactively. Runtime monitoring catches attacks that were not anticipated during testing and blocks them in production. Neither is sufficient on its own: a deployment with red teaming but no runtime monitoring is unprotected against novel attacks; one with runtime monitoring but no red teaming has no visibility into what weaknesses exist until an attack succeeds.

Conclusion

"AI security solution" describes a category, not a product. The category spans pre-deployment validation, continuous red teaming, and runtime monitoring. Most products on the market address one of these layers. The teams that get this right are the ones that start their evaluation with layer coverage rather than feature lists.

Repello's platform is built to address all three layers. ARTEMIS runs continuous attack batteries against deployed AI systems across the full MITRE ATLAS and OWASP LLM Top 10 attack surface. ARGUS monitors production systems and blocks active exploitation attempts in real time. AI Asset Inventory provides visibility into what AI is running in your environment so you know what you are securing.

To see how Repello's AI security solution covers the full three-layer model in practice, request a demo.