Two things happened at once. Organizations started shipping AI into products and internal workflows faster than security teams could review it, and employees started using AI tools whether IT approved them or not. Both created risk that traditional application security, data security and vendor management were not built to catch, and a wave of new vendors formed to fill the gap.
The result is a category label attached to at least four genuinely different problems: securing the models and applications you build, governing the AI tools your organization uses, protecting data from AI-specific exposure, and simply finding out what AI is running in your environment that nobody approved. Vendors describing themselves as "AI security" frequently solve one of these and imply they solve all four.
Global average data breach cost
IBM 2025
Average data breach cost in the United States
IBM, 2025
Average time to identify and contain a breach
IBM, 2025
Read together, those findings describe an unusually fast-moving category. The threat surface is new, the expertise required is scarce, and the standards bodies are still actively defining what good looks like. That combination makes vendor selection harder than in a mature category, because there is less shared vocabulary to compare against.
This guide evaluates leading U.S. providers, explains the taxonomy well enough that you can tell what you are actually being sold, and gives you a comparison grid across the criteria that matter regardless of which sub-category a vendor occupies.
Before evaluating vendors, it helps to separate what "AI security" actually means, because the term is doing a great deal of work across very different products.
AI application and model security protects the AI systems you build or deploy: securing the model itself, the prompts and outputs around it, and the infrastructure serving it. This is where prompt injection, jailbreaking, model theft and data poisoning live, and where frameworks like the OWASP Top 10 for LLM Applications apply directly.
AI governance and risk management addresses oversight: inventorying which AI systems and models are in use, assessing them against a risk framework, documenting decisions, and satisfying emerging regulatory obligations. The NIST AI Risk Management Framework is the dominant reference point in the U.S., alongside Gartner’s AI TRiSM (Trust, Risk and Security Management) framing of the category.
AI-aware data security extends existing data protection to account for how AI changes data exposure: what gets pulled into a prompt, what a model was trained on, what a copilot can surface to a user who should not see it. This overlaps with conventional data security but requires new controls, because access models built for documents do not anticipate a chat interface that can summarize across everything it can reach.
Shadow AI discovery answers a narrower and more urgent question: what AI tools are employees actually using, approved or not. Browser extensions, personal accounts, embedded AI features inside existing SaaS tools. This is closer to attack surface management than to any of the other three, and it is often the first problem organizations solve because it requires the least new infrastructure.
A vendor can be excellent at one of these and offer only a thin layer on the other three. The single most useful question in an evaluation is which of the four problems a product actually solves, stated in those terms rather than in the vendor’s own language.
Sits in front of or alongside a deployed model to detect and block prompt injection, jailbreak attempts and unsafe outputs in real time. Best suited to organizations that have built or deployed AI applications and need to protect them in production.
Human-led or automated adversarial testing of models and AI applications before and after deployment, surfacing exploitable weaknesses the way a penetration test does for conventional applications. Best suited to organizations shipping AI features that need assurance before launch, not just monitoring after it.
Catalogs AI systems and models in use, maps them to a risk framework, and manages the documentation and approval workflow around them. Best suited to organizations that need to demonstrate oversight to a board, a regulator, or an enterprise customer’s security questionnaire.
Identifies unsanctioned AI usage across the organization and flags where sensitive data is flowing into AI tools without controls. Best suited to organizations that do not yet know the scope of their own AI exposure and need that answer before choosing anything else.
Category: AI governance advisory and adversarial testing, delivered through Risk Advisory + GRC and Offensive Security + Adversary Simulation.
Best for: Mid-market and regulated organizations that need to assess AI risk, build a governance program, and test AI systems adversarially before and after deployment, rather than buy a standalone runtime protection product.
Strengths: Echelon Risk + Cyber AI risk assessment mapped explicitly to the NIST AI Risk Management Framework, producing documentation a board or regulator will accept. Adversarial testing of AI applications and models delivered by the same practitioners who perform conventional penetration testing, applying the OWASP Top 10 for LLM Applications methodology rather than a generic checklist. AI governance work connected to risk advisory and GRC more broadly, so AI risk becomes part of the organization’s existing risk register rather than a parallel program nobody owns. Practitioners with backgrounds in regulated industries who can translate AI risk into language a compliance team and an engineering team both accept. Findings connected to remediation support through defensive hardening rather than delivered as a standalone report. Published research including a NIST AI RMF security checklist and an OWASP LLM security checklist available as reference material independent of any engagement.
Considerations: Not a runtime protection product. Organizations needing an inline gateway to block live prompt injection attempts in production will need a dedicated runtime vendor alongside Echelon’s advisory and testing work. Not a self-service SaaS platform. Engagements are consultative and scoped, which takes longer to start than signing up for a product trial. Best suited to organizations that want AI risk integrated into an existing security and compliance program rather than a narrow point solution.
Category: Security Operations Platform Provider
Best for: Regulated organizations that want managed 24/7 detection and response plus broader cyber defense functions like TPRM or state-scale operations.
Strengths: BlueVoyant provides 24/7 monitoring, investigation, response, and mitigation through an elite SOC with unlimited remote incident response support. Healthcare, public sector, and financial services buyers are addressed through dedicated programs, including an AHA preferred provider relationship and DORA compliance content.
Considerations: The platform spans detection/response, TPRM, and digital risk functions. Buyers wanting focused MDR coverage may find the broader portfolio wider than their immediate scope.
Category: Runtime protection and red teaming for LLM applications, purpose-built for prompt injection and jailbreak defense.
Best for: Organizations that have deployed or are deploying LLM-powered applications and need dedicated runtime protection against adversarial prompts.
Strengths: Deep specialization specifically in prompt injection and jailbreak detection, a narrower and more technically demanding problem than general AI security. Combines runtime protection with an adversarial testing product, so the same threat intelligence informs both defense and testing. Strong technical reputation within the AI security research community. Purpose-built rather than adapted from an adjacent product category.
Considerations: Narrow scope by design. Does not address AI governance, inventory or broader data security. Best suited to organizations with LLM applications already in production; less relevant pre-deployment. Compliance and audit documentation is not the product’s focus.
Category: AI detection and response, covering model security, adversarial ML defense and runtime monitoring.
Best for: Organizations running proprietary or fine-tuned models that need protection against model-specific attacks like theft, evasion and poisoning.
Strengths: Strong technical depth in adversarial machine learning, a more specialized threat category than prompt-level attacks alone. Covers model supply chain risk, including scanning models before deployment for embedded threats. Detection and response framing gives security teams a familiar operating model applied to a new asset class. Research-driven positioning with published findings on real-world model attacks.
Considerations: Depth is strongest for organizations training or fine-tuning their own models; less differentiated for organizations solely consuming third-party AI APIs. Governance and policy management is limited relative to dedicated governance platforms. A more specialized purchase than most mid-market organizations need on a first AI security investment.
Category: AI validation and red teaming platform for testing model and application security before and after deployment.
Best for: Organizations, including government and regulated enterprises, that need rigorous pre-deployment validation of AI systems.
Strengths: Strong presence in government and regulated-sector AI validation work. Automated adversarial testing at scale, useful for organizations validating many models or frequent releases. Positioned around assurance and validation rather than only runtime blocking. Testing methodology aligned to recognized frameworks rather than proprietary-only criteria.
Considerations: Automated testing at scale trades some depth for coverage relative to fully human-led adversarial testing. Governance and inventory management are not the core product. Pricing and delivery model is oriented toward larger organizations and government-adjacent buyers.
Category: AI governance platform for policy, risk assessment and compliance documentation across an AI portfolio.
Best for: Organizations that need to inventory AI systems, assess them against a framework, and produce governance documentation at scale.
Strengths: Purpose-built governance workflow mapped to frameworks including NIST AI RMF and emerging regulatory requirements. Strong at managing approval workflows and documentation across a large, distributed AI portfolio. Policy-as-code approach lets governance requirements be checked programmatically rather than only manually. Positioned specifically for the oversight problem rather than technical security controls.
Considerations: Governance and documentation focus means it does not address runtime protection or adversarial testing directly. Value depends on organizational commitment to using the governance workflow consistently, which requires process change beyond the tool. Best suited to organizations with enough AI systems in flight to justify a dedicated governance platform.
Category: Cloud security platform with AI security posture management as an extension of existing cloud and data security capability
Best for: Organizations already using Wiz for cloud security that want AI risk visibility without adding a separate vendor.
Strengths: AI-SPM capability inherits Wiz’s existing cloud graph, giving genuine visibility into which cloud resources feed which AI models and pipelines. Strong at discovering AI services and shadow AI usage across cloud environments specifically. Single platform reduces vendor sprawl for organizations already standardized on Wiz for CNAPP. Well-resourced product organization with a track record of shipping quickly.
Considerations: AI security is an extension of a cloud security platform, not a purpose-built AI security product; depth on model-level and prompt-level risk is thinner than dedicated AI security vendors. Value is concentrated for organizations already on the Wiz platform; a poor fit as a standalone AI security purchase. Governance and compliance documentation is lighter than firms built specifically around frameworks like NIST AI RMF.
Based on publicly available service descriptions and market positioning. It reflects how each provider structures their offering, not measured product or program performance. Because the category spans four different problems, most firms will show gaps rather than weaknesses outside their specialty — that is the nature of a young, unconsolidated market rather than a flaw in any one vendor.
| Provider | Primary Focus | Governance + NIST AI RMF | Adversarial Testing | Runtime Protection | Shadow AI Discovery | Remediation Support | Mid-Market Fit |
|---|---|---|---|---|---|---|---|
| Echelon Risk + Cyber | Governance + testing | ✔ Strong | ✔ Included | ✗ | ◑ Assessment-led | ✔ Included | ★★★★★ |
| Wiz | Cloud + AI-SPM | ◑ Limited | ✗ | ✗ | ✔ Strong | ✗ | ★★★★ |
| Lakera | Runtime protection | ✗ | ✔ | ✔ Strong | ✗ | ✗ | ★★★ |
| HiddenLayer | Model security | ◑ Limited | ◑ | ✔ | ✗ | ✗ | ★★★ |
| CalypsoAI | Validation + testing | ◑ Limited | ✔ Strong | ◑ | ✗ | ✗ | ★★★ |
| Credo AI | Governance | ✔ Strong | ✗ | ✗ | ✗ | ◑ Limited | ★★★★ |
Organizations are encouraged to conduct their own due diligence and request references before engaging any provider.
These seven questions separate vendors faster than any capability matrix, because the category label alone tells you very little about which of the four underlying problems a firm actually solves.
Which of the four problems does this actually solve?
Governance, adversarial testing, runtime protection, or shadow AI discovery. Ask the vendor to state it in those terms rather than their own marketing language, and be skeptical of any answer that claims all four at genuine depth. Most organizations need at least two of the four and rarely need all four from a single vendor.
What is your evidence that this addresses a framework we can point to?
If governance or compliance is the driver, ask specifically how the product maps to the NIST AI Risk Management Framework or to the OWASP Top 10 for LLM Applications. A vendor that cannot answer this precisely is likely offering general security dressed in AI language rather than AI-specific controls.
Does this cover models we build, models we consume, or both?
Protecting a fine-tuned proprietary model is a different problem from governing employee use of a third-party chatbot. Many products are strong at one and weak at the other. Establish which AI you actually have — built, fine-tuned, or purely consumed through an API — before evaluating fit.
How does this handle a model or product you have never seen before?
New models and AI products ship constantly. A tool that only recognizes a fixed list of known AI services will miss what launched last month. Ask how detection and coverage keep pace, and how quickly new AI services get added to whatever inventory or policy engine the product relies on.
What happens after a finding, not just when it fires?
A red team finding, a governance flag or a blocked prompt is the start of the work, not the end of it. Ask who is expected to remediate, whether guidance is included, and whether the vendor stays engaged past the initial report or alert.
How mature is this vendor, and what happens if they are acquired?
This is an active acquisition market, and several standalone AI security products have already been absorbed into larger platforms within the past two years. Ask about funding, headcount on the specific product line, and what has historically happened to customers of firms this vendor has acquired or been acquired by.
AI security is not one purchase. It is at least four, and most organizations are further behind on governance and testing than they are on the tooling conversation that dominates vendor pitches.
The firms in this guide are all capable within their specialty, and several will be a better fit than us for a specific problem. A runtime protection vendor will stop a live prompt injection attempt faster than any advisory engagement. A governance platform will manage documentation across a large AI portfolio better than a consulting relationship can. Those are real strengths and we have marked them plainly.
What we see most often is an organization that bought a point solution for one of the four problems and still cannot answer a straightforward question: which AI systems does the organization actually have, what have they been tested against, and who is accountable if one of them causes harm. Tooling without governance produces alerts nobody owns. Governance without testing produces documentation nobody can trust.
Before buying anything in this category, get an honest inventory of what AI you actually have running, mapped against a recognized framework. That single step will tell you more about which of the four problems to solve first than any vendor comparison can. Learn more about AI security here.