HiddenLayer vs. Lakera vs. Prisma AIRS: Key Differences for AI Red-Teaming Tools

AI red-teaming, also called LLM security testing or adversarial AI testing, is the practice of deliberately attacking a language model, GenAI application, or AI agent before it reaches production, using the same techniques a real attacker would use, so vulnerabilities become logged findings instead of live incidents. The core attack categories these platforms probe for include prompt injection (getting a model to ignore its system instructions, either directly through user input or indirectly through content the model reads from a document, website, or tool output); jailbreaks (bypassing safety guardrails to produce disallowed output); data and model extraction (pulling training data, system prompts, or proprietary weights back out through crafted queries); and unsafe or non-deterministic behavior more broadly, since a large language model does not necessarily fail the same way twice given the same input.

This is a different job than a conventional web application penetration test. A model can pass every standard security check, patched dependencies, no SQL injection, correct authentication, and still be trivially jailbroken, because the vulnerability lives in the model's own weights and behavior rather than in the surrounding application code. That gap is what HiddenLayer, Lakera, and Palo Alto Networks' Prisma AIRS are each built to close, though they approach it from different starting points, as described below. For a broader view of how this risk fits into an enterprise security program overall, see the enterprise AI and LLM security guide; for how the same risk shows up when AI-generated code ships without review, see the vibe coding security risks article. Neither of those pieces goes deep on vendor selection for a dedicated red-teaming purchase, which is the narrower question this guide answers.

Both Lakera and Prisma AIRS sit inside larger corporate parents now (Check Point and Palo Alto Networks, respectively), which changes how a purchase decision should be framed compared with HiddenLayer, still an independent company as of this writing.

The biggest practical difference between these three in 2026 is not detection technique alone. All three test overlapping attack categories: prompt injection, jailbreaks, extraction, and unsafe behavior. The bigger difference is company structure and how tightly the red-teaming module is bound to a broader platform.

HiddenLayer remains an independent, privately held company (it raised a total of $56 million through its 2023 Series A) whose entire business is AI security. Its Automated Red Teaming module, launched in November 2024, is explicitly designed to close a loop with the company's own AI Detection & Response (AIDR) runtime product: findings from a red-team run feed into AIDR to generate runtime detection rules, so a confirmed jailbreak pattern can become a monitored rule without leaving the platform. That tight internal loop, buying both testing and detection from one AI-native vendor, is HiddenLayer's core architectural bet.

Lakera built its reputation as an independent Zurich-based company (its self-published Prompt Injection Test, or PINT, benchmark and its integration with NVIDIA's NeMo Agent Toolkit both date from that period) before Check Point Software Technologies acquired it in September 2025 for a reported $300 million, completing the deal in the fourth quarter of that year. Lakera now operates as the foundation of Check Point's AI security research and development, rather than as a fully standalone company, and its former product names (Lakera Guard for the runtime layer, Lakera Red for adversarial testing) are increasingly folded into Check Point branding, even though the underlying capabilities have not obviously changed.

Palo Alto Networks completed its acquisition of Protect AI in July 2025. Palo Alto Networks did not disclose an official deal value; third-party reporting placed estimates roughly between $500 million and $700 million, figures that should be read as outside estimates rather than a confirmed number. Since closing, Palo Alto Networks has folded Protect AI's separate products, Guardian (model scanning), Recon (red teaming), and Radar (AI asset inventory), into a single Prisma AIRS platform alongside Palo Alto's own AI Runtime Firewall and AI Runtime API. Prisma AIRS 3.0, announced in March 2026, extended that consolidation to agentic AI workflows, and an AI Gateway component reached general availability in July 2026. The practical effect for a buyer: Protect AI is no longer evaluable as an independent option, only Prisma AIRS as a module inside Palo Alto Networks' wider security portfolio.

HiddenLayer. Delivered as a SaaS AISec Platform with distinct modules. AIDR monitors GenAI and traditional ML models in production, described by the vendor as non-invasive, meaning it does not inject additional data or add measurable performance overhead to the model it watches. Automated Red Teaming runs scheduled or on-demand scans across LLMs, agents, and predictive models, producing vulnerability scores and trend data a team can track over time; scans can be triggered manually or on a recurring schedule, and re-run after a system change such as a model upgrade or new system prompt. Model Scanner focuses on supply-chain risk, checking model files themselves, not just runtime behavior, for embedded malicious code. Public materials do not detail specific on-premise or air-gapped deployment options, so buyers with that requirement should confirm it directly with the vendor.

Lakera. Delivered as a cloud platform with API and SDK-based integration, plus a free community tier for individual developers alongside a custom enterprise plan. AI Red Teaming (the current name, with "Lakera Red" persisting as a legacy URL) runs a three-step workflow: scope the target AI system, simulate adversarial interactions against it, and surface vulnerabilities across three categories the vendor calls safety, security, and responsible AI, covering context extraction, instruction-override attacks, content injection, service disruption, and indirect poisoning. Lakera also sells an expert-led AI Red Teaming Service for organizations that want a human-run engagement instead of, or alongside, the automated product. Like HiddenLayer, public pages do not spell out on-premise deployment; confirm data residency and hosting directly, particularly given the recent change in corporate ownership.

Prisma AIRS. The broadest deployment footprint of the three, reflecting its position inside a much larger network and cloud security portfolio. Components include an AI Runtime Firewall that inspects traffic for prompt injection, sensitive data leakage, insecure output, and model denial-of-service; an AI Runtime API that lets developers embed the same checks directly in application code; AI Model Security (built from Guardian) that scans model files before deployment for arbitrary code execution, deserialization threats, and neural backdoors; and an AI Red Teaming module (built from Recon) that sends crafted attack prompts to a target system and evaluates responses for compromised content. Deployment spans multi-cloud environments, configured through Strata Cloud Manager, with additions such as Azure multi-account onboarding, KVM cluster support for certain components, and the AI Gateway that reached general availability in July 2026. This is the only one of the three with the kind of on-premise, network-appliance-style deployment that organizations already standardized on Palo Alto Networks tend to expect from the rest of their stack.

All three vendors describe API-first integration, but public documentation is thin on the exact CI/CD and MLOps hooks each provides, so this is an area to test directly in a proof of concept rather than take from a data sheet.

HiddenLayer's Automated Red Teaming is designed to run automatically on a schedule or on demand, which implies a trigger model suited to being called from a CI/CD pipeline after a model or system-prompt change, and its stated closed loop from red-teaming findings into AIDR detection rules is itself a form of integration between HiddenLayer's own modules. Public materials do not list specific SIEM connectors, so SIEM and SOAR forwarding should be confirmed directly.

Lakera markets strong developer ergonomics, with API and SDK access described as central to both its testing and runtime products, and highlights a partnership with NVIDIA's NeMo Agent Toolkit specifically for red-teaming agentic AI built on that framework. That integration matters if a team is building agents on NVIDIA's stack and is not relevant otherwise.

Prisma AIRS has the deepest built-in integration story of the three by virtue of sitting inside Palo Alto Networks' existing platform: it connects to Strata Cloud Manager for centralized configuration, documents support for Microsoft Copilot Studio and other foundation-model platforms, and its AI Runtime API is explicitly built for developers to call directly from application code. Organizations that already forward Palo Alto Networks telemetry into a SIEM are more likely to see Prisma AIRS findings land in that same pipeline with less custom work than a net-new vendor would require.

In all three cases, ask for a live demonstration of a specific CI/CD trigger, for example, a pull-request check that fails a build above a chosen severity threshold, and the specific SIEM/SOAR forwarding format, rather than accepting "API available" as sufficient evidence of a working integration.

Running any of these tools well requires more than turning it on. Expect roughly this operational shape from each:

HiddenLayer's automated, schedulable red-teaming is designed to reduce the manual labor of routine testing, and its stated non-invasive runtime monitoring is built to avoid adding meaningful latency or requiring model retraining, but a security or ML engineering owner still needs to triage findings, decide what severity threshold blocks a release, and confirm that new detection rules generated from red-team findings do not create runtime false positives that block legitimate traffic.

Lakera's continuous-workflow framing (scope, simulate, identify) similarly reduces manual test-authoring effort compared with building an internal red-teaming harness from scratch, but findings across its three risk categories still need a human owner to prioritize, and some findings, such as legal or compliance exposure under the responsible AI category, require input from stakeholders outside the security team.

Prisma AIRS carries the heaviest operational surface area of the three because it is the most componentized: a team adopting the AI Runtime Firewall, AI Runtime API, AI Model Security, and AI Red Teaming together is effectively standing up four related but distinct capabilities, each configured inside Strata Cloud Manager. That is a reasonable tradeoff for a team that already operates Palo Alto Networks infrastructure and has staff comfortable in that console, and a heavier lift for a team that does not.

Across all three, budget for a tuning period. No vendor's default severity thresholds or attack-prompt library will match a specific application's actual risk tolerance out of the box.

None of the three vendors publishes public per-seat, per-model, or tiered pricing for their red-teaming products. HiddenLayer requires a direct sales conversation for its AISec Platform, including Automated Red Teaming. Lakera offers a free community tier for individual developers and researchers, but its enterprise AI Red Teaming and AI Agent Security products require a custom quote, and pricing has likely shifted since Check Point's 2025 acquisition, so third-party pricing estimates found online that predate that ownership change should not be trusted. Prisma AIRS pricing is folded into Palo Alto Networks' broader Prisma Cloud and Strata licensing conversations, meaning a standalone "Prisma AIRS-only" quote in isolation from an organization's existing or prospective Palo Alto Networks contract is unlikely. Get a written, scoped quote directly from each vendor based on the number of models or applications tested, testing frequency, and, for Prisma AIRS, how it fits into an existing or new Palo Alto Networks agreement, rather than relying on any published estimate.

HiddenLayer strengths: a genuinely AI-native, independent company whose entire roadmap is AI security, with automated red-teaming, runtime detection (AIDR), and model/supply-chain scanning built to work together as one closed loop; a stated non-invasive approach to runtime monitoring; and institutional customers across finance, government, defense, and cybersecurity verticals, per the vendor.

HiddenLayer limitations: smaller total funding ($56 million) and likely smaller headcount than a platform-backed competitor, which may matter to buyers who weight vendor scale and long-term roadmap certainty; public documentation is light on specific CI/CD and SIEM connector details, so integration depth needs direct verification; and, like its competitors, HiddenLayer's own performance and detection claims have not been independently, third-party benchmarked in public reporting reviewed for this article.

Lakera strengths: strong developer-facing ergonomics and API/SDK design built up over several years as an independent company; a self-published PINT benchmark specifically for evaluating prompt-injection detectors, on which the vendor reports its own Guard product scored above 97%; a specific integration with NVIDIA's NeMo Agent Toolkit for agentic red-teaming; and now, backing from Check Point's broader security research and go-to-market resources.

Lakera limitations: the PINT benchmark is Lakera's own creation, not an independently administered third-party test, so treat that score as a vendor-favorable data point to validate independently rather than a settled industry ranking; the September 2025 acquisition by Check Point means product roadmap, support model, and pricing are now set by a much larger company's priorities, a meaningful change for existing customers to watch; and, as with HiddenLayer, the vendor's stated capabilities have not been independently audited in public reporting reviewed here.

Prisma AIRS strengths: the broadest deployment footprint of the three, spanning a runtime firewall, an embeddable runtime API, pre-deployment model scanning (from Guardian), and automated red-teaming (from Recon), all under one console; the deepest bundling potential for organizations already running Palo Alto Networks' Prisma Cloud, Strata, or next-generation firewall products; and continued, visible investment, with Prisma AIRS 3.0 (March 2026) and a generally available AI Gateway (July 2026) shipped within the last year of this writing.

Prisma AIRS limitations: it is the newest of the three to have red-teaming fully folded into a single platform, since Recon, Guardian, and Radar were only integrated after the July 2025 acquisition closed, so the red-teaming module specifically has a shorter track record under its current branding than HiddenLayer's or Lakera's do under their own; standalone pricing separate from a broader Palo Alto Networks agreement is unlikely to be available; and organizations with no existing Palo Alto Networks footprint take on a much larger platform than they may need if red-teaming is the only capability they actually want.

HiddenLayer tends to fit organizations that want a single, AI-native vendor covering the full loop from adversarial testing to runtime detection to model supply-chain scanning, without tying that purchase to a much larger, unrelated security platform, and that are comfortable buying from an earlier-stage, independent company rather than a division of a larger public security vendor.

Lakera tends to fit teams that prioritize a fast, developer-friendly red-teaming workflow with strong API and SDK ergonomics, especially those building agentic AI on NVIDIA's NeMo stack specifically, and that are comfortable with the product now being owned and roadmapped by Check Point Software Technologies rather than an independent Lakera.

Prisma AIRS tends to fit organizations already standardized on Palo Alto Networks for network, cloud, or SASE security, where bundling AI red-teaming with a runtime firewall, model scanning, and agent security under one existing vendor relationship and console (Strata Cloud Manager) reduces the number of new vendor relationships a security team has to manage, even if any individual Prisma AIRS module is not the newest or most specialized option on the market.

All three fits should be confirmed against an organization's own environment. Marketing pages describe intended use cases; only a proof of concept against a real model and application confirms which one actually performs better for a specific team.

A dedicated AI red-teaming platform is not always the right next purchase. Consider skipping all three vendors, at least for now, if:

• There is no production LLM application yet. If no live GenAI app, agent, or model is serving real traffic, only internal experimentation, a dedicated red-teaming platform is premature spend. A manual review against the OWASP Top 10 for Large Language Model Applications and a plan to revisit tooling once something reaches production is a reasonable interim step.
• The team is too small to act on findings. Automated red-teaming produces a stream of findings that someone has to triage, prioritize, and fix. A one- or two-person security function already stretched across other duties may get more value from a smaller, focused manual assessment or a single expert-led engagement, such as Lakera's advisory service, than from an always-on automated platform generating a backlog no one has time to close.
• The actual need is a runtime firewall, not a testing tool. Red-teaming finds vulnerabilities before deployment; it does not stop an attack against a live system. If the real gap is production protection against prompt injection or data leakage right now, evaluate the runtime side of these platforms directly (HiddenLayer's AIDR, Lakera's AI Agent Security, or Prisma AIRS' AI Runtime Firewall), rather than starting with the red-teaming module. See the enterprise AI and LLM security guide for the broader set of runtime and governance controls that sit alongside red-teaming. If the specific gap is employees pasting confidential data into ChatGPT, Copilot, or Claude rather than an adversary attacking a model an organization built itself, a dedicated shadow AI data-leak prevention comparison covers that narrower, more common problem.
• Budget and procurement already favor a bundle in place. If an organization already pays for Palo Alto Networks' Prisma Cloud or Check Point's broader security suite and has not activated the AI security modules, confirm what is already included or discounted under the existing contract before treating a standalone HiddenLayer or Lakera purchase as the default option.

Run any AI red-teaming proof of concept against a real model and application, not a vendor-provided demo environment. Before committing budget, confirm the following with each vendor side by side.

Most vendor-evaluation checklists ask generic questions (support SLAs, uptime, compliance certifications) that apply to any security tool. For this specific category, the more diagnostic questions are about benchmark rigor, ownership stability, and what data actually leaves the environment.