Lakera vs Prisma AIRS vs Cisco AI Defense: $1B Deals [2026]

Three of the biggest names in cybersecurity spent close to a billion dollars in 2025 buying their way into a category that barely existed three years ago: AI guardrails. Check Point closed its purchase of Lakera in October 2025. Palo Alto Networks folded Protect AI into a rebuilt Prisma AIRS in the same window. Cisco had already absorbed Robust Intelligence a year earlier, turning it into the backbone of Cisco AI Defense. By September 2026, all three platforms are live, all three are selling to the same enterprise buyers, and none of them publish a public price list. The bet behind all that spending looks better by the month: Future Market Insights, in a forecast published September 2026, projects the AI security platform category will keep expanding at a 22.0% compound annual growth rate, which is exactly the kind of runway that pushed three cybersecurity incumbents to buy rather than build. This is a comparison of what each one actually does, what it costs to find out, and which one fits a given security team’s stack.

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The AI Security Guardrail Market Just Consolidated Into Three Giants

Eighteen months ago, AI security guardrails were a startup category. Lakera, Robust Intelligence, and Protect AI each raised venture rounds selling standalone products that screened prompts, tested models for jailbreaks, and flagged data leakage in generative AI applications. That era is over. Cisco announced its intent to acquire Robust Intelligence on August 26, 2024, and confirmed the deal closed on September 24, 2024. Palo Alto Networks announced its move on Protect AI on April 28, 2025, and completed the purchase on July 22, 2025. Check Point announced its agreement to acquire Lakera on September 16, 2025, and finished the deal on October 22, 2025.

None of the three acquirers has published an official, audited purchase price for all three deals. Check Point’s FY2025 Form 20-F is the closest thing to a hard number in this category: it puts total consideration for Lakera at approximately $201.8 million, well under the roughly $300 million figure that circulated in press coverage when the deal was announced. Palo Alto Networks has never confirmed Protect AI’s price. Reporting from Globes and BankInfoSecurity pegged it at $650 million to $700 million before close, while a Yahoo Finance report characterized it as “over $500 million.” Cisco disclosed nothing for Robust Intelligence. 451 Research estimated the deal at $350 million, while Calcalist reported $400 million citing sources close to founder Yaron Singer. Add the most conservative sourced figures together (Check Point’s audited $201.8 million, Palo Alto’s $500 million-plus estimate, and Cisco’s $350 million estimate) and the market has spent north of $1 billion buying AI guardrail technology in roughly a twelve-month span.

The pattern matters more than any single number. Buyers evaluating an AI security platform in September 2026 are no longer choosing between scrappy startups. They are choosing which cybersecurity incumbent they want handling AI risk alongside their existing firewall, endpoint, and cloud contracts. That is a fundamentally different purchasing decision, and it changes how each platform gets priced, packaged, and supported.

What AI Security Guardrail Platforms Actually Do

Before comparing vendors, it helps to define the category. An AI security platform sits between a large language model or AI agent and the outside world, watching both directions of traffic. On the way in, it screens prompts for injection attempts, jailbreak patterns, and attempts to exfiltrate a system prompt. On the way out, it screens model responses for sensitive data leakage, unsafe content, malicious links, and signs that an agent is about to take an action it shouldn’t. A separate but related function is red teaming: continuously attacking a company’s own models and agents to find weaknesses before an outside attacker does. A third function, model security scanning, checks third-party and open-source models for backdoors, embedded malware, or data poisoning before they ever reach production. The category isn’t limited to the three acquirers profiled here, either: independent vendors keep shipping standalone platforms alongside them, Codenotary launched AgentMon 3 in July 2026, its latest enterprise AI-security-platform generation, a reminder that the guardrail market still has room for players who haven’t been bought by a cybersecurity incumbent.

All three platforms in this comparison, Check Point’s Lakera-based offering, Palo Alto’s Prisma AIRS, and Cisco AI Defense, cover some combination of these functions. None of them are identical, and the differences trace directly back to what the acquired startup was originally built to do. Lakera started as a runtime guardrail company known for its public “Gandalf” prompt-injection game. Robust Intelligence built its name on algorithmic red teaming and model validation. Protect AI shipped four distinct products, Recon for red teaming, Radar for AI bill-of-materials tracking, Guardian for model scanning, and Layer for real-time monitoring, before Palo Alto absorbed it.

Check Point’s Lakera Acquisition: Inside the $201.8M Deal

Check Point announced its agreement to acquire Lakera AI AG on September 16, 2025, framing the deal around building “a full end-to-end AI security stack for AI-powered enterprises.” Check Point’s own press release did not disclose financial terms at announcement, and pre-close media coverage from outlets including ET Telecom and VentureLab Swiss cited an estimated $300 million valuation. The deal closed on October 22, 2025. When Check Point filed its FY2025 Form 20-F with the SEC, the note on the acquisition put total consideration at approximately $201.8 million, allocating $150.1 million to goodwill and $44 million to core technology, a materially lower figure than the pre-close press estimate.

Lakera is not the only AI-security company Check Point has bought. According to a WWT analysis of Check Point’s strategy, the company spent nearly $340 million combined pulling Lakera, Cyata, Cyclops, and Rotate into its Infinity platform as part of what it calls a unified “AI Defense Plane.” As of late July 2026, the Lakera brand and lakera.ai website were still live and unredirected, still describing itself as “The AI-Native Security Platform,” with a footer crediting “Check Point Software Technologies Ltd.” Functionally, this means Lakera today operates as a Check Point product line under its original name rather than a fully rebranded Check Point SKU, similar to how Check Point has historically kept acquired brand names (CloudGuard, Harmony) visible after a deal closes.

On the technical side, Lakera’s engineering team has kept shipping updates on Check Point’s dime. Its August 2026 changelog documentation shows the Guard API now returns a five-tier confidence breakdown per detector (l1_confident through l5_unlikely), giving application teams more granular signal than a binary block-or-allow response. A mid-2026 changelog entry added a dedicated self-harm sub-category to Lakera’s content moderation detector, and a separate release patched security issues in Lakera’s gateway component, tied to encryption and policy-resolution issues. That patch cadence is itself a data point: Lakera is still being actively developed as a distinct engineering team inside Check Point, not simply absorbed and shelved.

Palo Alto’s Prisma AIRS: How Protect AI Became a $700M Bet

Palo Alto Networks announced its intent to acquire Protect AI on April 28, 2025, describing it as “a game-changing security for AI” company. The completion press release confirms the deal closed on July 22, 2025. As with the other two acquisitions, Palo Alto never confirmed a purchase price. BankInfoSecurity reported Palo Alto was “prepared to pay between $650 million and $700 million,” citing Globes. A separate Yahoo Finance report framed the same transaction as “$500M+.” Palo Alto’s own Cyberpedia page on its strategic acquisitions lists Protect AI with the same “$650-700 million” estimate range, explicitly labeled as an estimate rather than a disclosed figure.

Where Palo Alto has been unusually specific is product integration. On October 28, 2025, the company announced Prisma AIRS 2.0, describing it as completing “the native integration of the recently acquired Protect AI” and positioning it as “the industry’s most comprehensive AI security platform.” Prisma AIRS 2.0 organizes its capabilities into three named modules. AI Agent Security provides runtime defense against prompt injection, tool misuse, and malicious agent behavior along with shadow-AI discovery. AI Red Teaming runs continuous, autonomous attacks using what Palo Alto describes as more than 500 specialized attack techniques. AI Model Security performs deep architectural analysis on models to catch backdoors, data poisoning, and malicious code. Protect AI’s original four product names, Recon, Radar, Guardian, and Layer, do not appear anywhere in Palo Alto’s Prisma AIRS 2.0 documentation, suggesting the underlying technology was absorbed into the new module structure rather than kept as standalone, separately branded products.

Palo Alto has kept expanding this stack past the Protect AI deal. Its own 2026 press page lists a completed acquisition of Portkey, announced around late July 2026, specifically to “secure AI agents,” suggesting Prisma AIRS is still being actively built out through further M&A rather than treated as a finished product. Separately, Palo Alto’s AI Runtime Security documentation describes an “AI Runtime Firewall” component that redirects workload traffic through Palo Alto’s next-generation firewall infrastructure, meaning Prisma AIRS ties directly into network hardware Palo Alto customers likely already own, a distribution advantage neither Check Point’s Lakera integration nor Cisco AI Defense currently matches at the same depth.

Cisco AI Defense: Robust Intelligence’s Path Into the Network

Cisco moved first. It announced intent to acquire Robust Intelligence on August 26, 2024, and posted a public update on its blog confirming the deal closed on September 24, 2024. Cisco disclosed no price at any point. 451 Research’s acquisition brief estimated the deal at $350 million. Calcalist’s report on founder Yaron Singer’s exit cited sources putting the figure at $400 million, a number several market-map profiles have since repeated as the de facto consensus estimate.

Cisco’s own product page states plainly that Robust Intelligence “has been foundational to the development of Cisco AI Defense and Cisco Foundation AI.” Unlike Check Point’s approach with Lakera, Cisco did not keep Robust Intelligence operating as a visible, separately branded product. The technology and team were folded directly into Cisco AI Defense, which launched roughly three months after the acquisition closed. A Cisco security leader’s public post from January 2025 described AI Defense as “an end-to-end AI security solution that protects the development and use of AI Applications,” explicitly crediting the Robust Intelligence acquisition as the technical foundation.

Cisco’s architectural pitch differs from both competitors in one specific way: it markets AI Defense as enforcing guardrails “inside the network without requiring agents or libraries in the application code.” Where Lakera and Prisma AIRS both lean on API-level integration into an application’s inference pipeline, Cisco’s positioning emphasizes network-layer enforcement, consistent with Cisco’s core business selling switches, routers, and firewalls. Cisco AI Defense is offered in both SaaS and self-hosted deployment models, a flexibility point Cisco highlights for regulated customers who cannot send AI traffic to a third-party cloud for inspection. A year after AI Defense’s initial launch, Cisco’s own blog described it as “the industry’s first truly comprehensive enterprise AI security solution” when introducing expanded agentic-AI coverage in 2026.

Lakera vs Prisma AIRS vs Cisco AI Defense: Full Specs Comparison

The table below lines up what is publicly documented about each platform as of September 2026. Where a vendor has not disclosed a figure, that is noted rather than estimated.

Attribute	Check Point (Lakera)	Palo Alto Networks (Prisma AIRS)	Cisco AI Defense
Acquired startup	Lakera AI AG	Protect AI	Robust Intelligence
Deal announced	Sept. 16, 2025	April 28, 2025	Aug. 26, 2024
Deal closed	Oct. 22, 2025	July 22, 2025	Sept. 24, 2024
Disclosed price	Not officially disclosed; audited 20-F puts total consideration at ~$201.8M	Not disclosed; estimates range $500M-$700M	Not disclosed; estimates range $350M-$400M
Current product name	Lakera (Check Point Infinity AI Defense Plane)	Prisma AIRS 2.0	Cisco AI Defense
Core modules	Guard API (prompt injection, jailbreak, content moderation, data leakage detection)	AI Agent Security, AI Red Teaming, AI Model Security	Algorithmic red teaming, runtime guardrails, model validation
Red-team attack coverage	Not publicly quantified	500+ specialized attack techniques (vendor-reported)	Not publicly quantified
Enforcement layer	Application/API-level runtime guard	Application-level plus AI Runtime Firewall (NGFW integration)	Network-level; no required app-code agents or libraries
Deployment options	Cloud/SaaS gateway	SaaS with optional NGFW routing	SaaS and self-hosted
Latest confirmed update	Aug. 2026 Guard API confidence-tier release	Portkey acquisition, ~July 2026	2026 agentic-era expansion (per Cisco blog)
Brand status	Original brand retained, live as of July 2026	Fully re-branded into Prisma AIRS	Fully absorbed into Cisco AI Defense
Parent platform ecosystem	Check Point Infinity	Prisma Cloud / Strata / Cortex	Cisco Security Cloud

The most important row for most buyers is enforcement layer. Cisco’s network-level approach means AI Defense can, in theory, cover traffic without touching application code, which appeals to security teams that do not control every AI deployment inside a large organization. Palo Alto’s dual approach, combining an application-level SDK with an AI Runtime Firewall that reroutes traffic through existing NGFW infrastructure, is the most complete on paper but assumes the customer already runs Palo Alto’s network hardware. Check Point’s Lakera integration remains the most developer-facing of the three, exposing a Guard API that application teams call directly, which suits organizations that want fine-grained, per-request control over what gets blocked.

Pricing and Licensing: What Enterprises Actually Pay

None of the three vendors publishes a public rate card for its AI security platform, which is itself a meaningful data point for buyers used to shopping SaaS tools with self-serve pricing pages. All three sell through enterprise sales motions with custom quotes, typically bundled into a broader platform contract (Check Point Infinity, Palo Alto’s Prisma/Strata/Cortex suite, or Cisco Security Cloud). That opacity persists even as the category keeps expanding, Future Market Insights forecasts a 22.0% CAGR for AI security platforms in a report published September 2026, which suggests all three vendors see enough incoming enterprise demand that a self-serve price list still isn’t necessary to win deals. The table below summarizes what is publicly known about how each platform gets sold, rather than what it costs, since exact figures are not published.

Vendor	Pricing model	Public price list	Typical bundling	Marketplace listing
Check Point (Lakera)	Custom enterprise quote	Not published	Sold as part of Check Point Infinity’s AI Defense Plane, alongside Cyata, Cyclops, and Rotate	No confirmed public marketplace SKU as of Sept. 2026
Palo Alto Networks (Prisma AIRS)	Custom enterprise quote	Not published	Sold standalone or bundled with Prisma Cloud/Cortex contracts	Legacy Protect AI modules (Recon, Radar, Guardian, Layer) were listed on AWS Marketplace under contract-based unit dimensions prior to full Prisma AIRS 2.0 integration
Cisco AI Defense	Custom enterprise quote	Not published	Sold as part of Cisco Security Cloud licensing	Available via Cisco’s direct and channel sales; no public self-serve tier

The acquisition prices themselves are the closest thing to a cost signal an outside buyer has. Check Point’s audited $201.8 million for Lakera, against Palo Alto’s estimated $500 million-plus for Protect AI and Cisco’s estimated $350 million-plus for Robust Intelligence, roughly tracks each acquirer’s overall market capitalization and appetite for AI security as a growth line. None of that translates directly into what a mid-size enterprise pays for a seat, an API call, or a per-agent license, because none of the three has published that number. Any comparison article claiming exact per-seat or per-token pricing for these three platforms as of September 2026 is not working from public information.

Capabilities and Self-Reported Coverage: No Independent Benchmark Exists Yet

Buyers naturally want a number: which platform blocks the highest percentage of prompt injections, or catches the most jailbreak attempts. As of September 2026, no independent, third-party benchmark comparing prompt-injection detection rates or jailbreak block rates across Check Point’s Lakera, Palo Alto’s Prisma AIRS, and Cisco AI Defense has been published by a recognized testing body. Benchmarks do exist elsewhere in the AI security world, for context, Microsoft’s MAI-Cyber-1-Flash paired with GPT-5.4 scored 95.95% on the CyberGym benchmark in July 2026, but that measures a model’s own offensive cyber capability, not how well a guardrail platform like Lakera, Prisma AIRS, or Cisco AI Defense actually blocks attacks against a deployed application. What exists instead for the guardrail vendors themselves is vendor-published, self-reported capability data, which is useful for understanding architecture but should not be read as a neutral scorecard.

Capability signal	Check Point (Lakera)	Palo Alto (Prisma AIRS)	Cisco AI Defense
Detection confidence granularity	Five-tier confidence scale per detector (l1_confident to l5_unlikely)	Not publicly broken into confidence tiers	Not publicly broken into confidence tiers
Automated red teaming	Not marketed as a standalone red-team product	500+ specialized attack techniques (vendor claim)	Algorithmic red teaming (vendor claim, no attack count published)
Content moderation categories	Includes a dedicated self-harm sub-category added in 2026	Not broken out publicly by category	Not broken out publicly by category
Model supply-chain scanning	Not a primary Lakera capability	Yes, inherited from Protect AI’s model-scanning technology	Yes, model validation was a core Robust Intelligence capability
Independent third-party benchmark	None published	None published	None published

The absence of independent benchmarks is not unusual this early in a product category’s life. EDR vendors went years before MITRE ATT&CK evaluations became a standard comparison point. AI guardrail platforms are roughly where EDR was around 2013: real products, real enterprise contracts, and a market still waiting for a neutral third party to run all three through the same attack suite and publish the results side by side.

Architecture Differences: Network-Level vs Application-Level Guardrails

The single biggest technical decision separating these three platforms is where enforcement happens. Cisco’s pitch is network-level: guardrails run inline with network traffic, so an application team does not need to add an SDK or call an external API before every model invocation. This lowers the integration burden but means Cisco is inspecting traffic patterns rather than reading deep into application-specific context, since it never sits inside the application’s own code path.

Palo Alto splits the difference. Prisma AIRS ships an application-level component for AI Agent Security and AI Model Security, but its AI Runtime Firewall specifically redirects workload traffic to Palo Alto’s next-generation firewall, meaning organizations get both a code-level integration option and a network-level fallback, provided they already run Palo Alto NGFW hardware. That dual-layer design is arguably the most complete architecture of the three, but it locks a customer more tightly into the broader Palo Alto stack than either competitor requires.

Check Point’s Lakera integration is the most purely application-level of the three. The Guard API is designed to be called directly from application code, wrapping every prompt and every model response with a synchronous or asynchronous check. This gives developers the most granular control, since each call can be tuned per use case, but it also means engineering teams must actively integrate Lakera’s SDK or API into every AI-powered feature they ship, rather than getting broad, automatic coverage from a network appliance already in place.

// Simplified example of an application-level guardrail call
// pattern, representative of how Guard-API style integrations
// wrap a model request (illustrative, not vendor SDK code)

const guardResult = await aiGuard.check({
  input: userPrompt,
  context: { appId: "support-bot", userId: currentUser.id }
});

if (guardResult.verdict === "block") {
  throw new Error(`Blocked: ${guardResult.detector} (${guardResult.confidence})`);
}

const modelResponse = await llm.generate(userPrompt);

const outputCheck = await aiGuard.check({
  input: modelResponse,
  direction: "output"
});

if (outputCheck.verdict === "block") {
  return fallbackResponse();
}

return modelResponse;

That pattern, checking both the inbound prompt and the outbound model response, is the common thread across all three vendors regardless of where enforcement physically happens. The difference is whether that check runs as an explicit API call inside application code (Check Point, and optionally Palo Alto) or as a transparent inspection point in the network path (Cisco, and optionally Palo Alto through its NGFW routing).

Real-World Use Cases: Why Enterprises Are Buying AI Guardrails in 2026

None of the three vendors has published named customer case studies for these specific AI security products as of September 2026. Palo Alto’s Prisma AIRS 2.0 announcement references only that the platform is “already trusted by global leaders in finance, healthcare, and government” without naming specific organizations. What is publicly documented, though, is a steady drumbeat of AI-related security incidents through 2026 that illustrate exactly why enterprises are signing these contracts in the first place.

• Agentic coding tool compromise. A security incident involving an AI coding assistant this year triggered 23 new governance rules for agentic AI systems, the kind of runtime-behavior monitoring that AI Agent Security modules in Prisma AIRS and equivalent Cisco AI Defense controls are built to catch before an agent takes an unauthorized action.
• Regional AI-agent breach at scale. Authorities in Taiwan reported AI agents used to crack 85 accounts in four days, a use case squarely inside the “malicious agent behavior” detection category every vendor in this comparison markets.
• Frontier-model risk escalation. When a frontier lab’s model reportedly hit a critical cyber-capability threshold, it prompted direct scrutiny from national security officials, underscoring why enterprises now treat red-teaming their own AI deployments as a standing requirement rather than a one-time pre-launch check.
• Cloud security posture consolidation. The same acquisition dynamic playing out in AI guardrails already happened in cloud-native security, where a $32 billion CNAPP acquisition reshaped the competitive field. Enterprises now expect their cloud security vendor to also own an AI security story, which is exactly what drove Palo Alto and Check Point to buy rather than build.
• Network-layer security convergence. Organizations already standardized on SASE platforms are watching how vendors like Palo Alto Networks bundle adjacent security categories into existing contracts, which is precisely the bundling motion Prisma AIRS follows inside the broader Palo Alto portfolio.

Each of these examples maps to a specific control category these platforms sell: agent behavior monitoring, prompt/response screening, red-teaming, and supply-chain model scanning. None of them confirms which specific vendor was or wasn’t involved, and readers should not infer that any named incident used, or would have been prevented by, any specific platform in this comparison. They illustrate the risk categories driving purchase decisions, not vendor performance claims.

Migration Guide: Moving From DIY Guardrails to a Platform

Most engineering teams building AI features in 2023 and 2024 wrote their own prompt-filtering logic: regex checks for known jailbreak phrases, a denylist of sensitive terms, maybe a call to a moderation API. That approach breaks down fast as the number of AI features grows, because every team ends up maintaining its own inconsistent version of the same checks. Moving to a managed AI security platform is less about buying new capability and more about consolidating scattered, ad hoc logic into one place. Here is a practical sequence for that migration, regardless of which of the three vendors above ends up in the shortlist.

1. Inventory every AI touchpoint. List every internal and customer-facing feature that calls an LLM or runs an autonomous agent, including ones built by individual product teams outside central engineering oversight.
2. Classify by risk, not by team. Group AI features by what they can do (read-only chat vs. an agent with write access to a database or payment system) rather than by which team built them.
3. Run a shadow-mode pilot. Deploy the guardrail platform in monitor-only mode against one high-traffic feature first, logging what it would have blocked without actually blocking anything, to calibrate false-positive rates before enforcement.
4. Compare shadow-mode findings against existing logs. Cross-reference what the platform flagged with any known incidents or near-misses your team already tracked manually, to sanity-check detection quality.
5. Migrate application-level integrations first. If choosing Check Point or the application-level half of Prisma AIRS, wire the Guard-API-style call into your highest-risk feature before touching lower-risk ones.
6. Layer in network-level coverage for blind spots. If choosing Cisco AI Defense, or the NGFW-routed half of Prisma AIRS, use network-level enforcement to catch AI traffic from features that were never inventoried in step one.
7. Set enforcement thresholds per feature, not globally. A customer support chatbot and an internal code-generation agent should not share the same block/allow sensitivity.
8. Establish an incident review loop. Route every block, and a sample of allowed traffic, to a weekly review so the security team can tune detectors instead of leaving default vendor settings in place indefinitely.
9. Decommission legacy DIY filters last. Keep old regex or denylist checks running in parallel for at least one full release cycle after the new platform goes live, purely as a safety net.
10. Re-audit quarterly. AI features and attack techniques both move fast. A guardrail configuration that made sense in Q1 2026 may miss techniques that emerged by Q3.

The sequence above applies whether the destination platform is Lakera, Prisma AIRS, or Cisco AI Defense, because the underlying problem, uninventoried and inconsistently protected AI features, is the same regardless of vendor. What changes is step 5 versus step 6: an application-level platform requires code changes per feature, while a network-level platform can catch unmonitored traffic without a developer ever touching the affected service.

Pros and Cons of Each Platform

Check Point (Lakera)

• Pro: Most developer-friendly API, with granular five-tier confidence scoring per detector for fine control over false positives.
• Pro: Original engineering team and brand still active and shipping updates, including CVE patches, as of August 2026.
• Pro: Lowest confirmed acquisition price of the three, which may translate into more aggressive enterprise pricing to build market share.
• Con: Smallest existing enterprise security footprint of the three parent companies, so it is a newer addition to an existing Check Point contract for most buyers.
• Con: No public red-teaming attack-count claim comparable to Palo Alto’s “500+ techniques” marketing.

Palo Alto Networks (Prisma AIRS)

• Pro: Most complete module set on paper, spanning agent security, red teaming, and model security under one roof.
• Pro: Dual enforcement layer (application-level plus AI Runtime Firewall through NGFW) gives redundant coverage.
• Pro: Actively expanding through further acquisitions (Portkey, mid-2026), signaling continued investment.
• Con: Highest estimated acquisition cost of the three, which historically correlates with premium enterprise pricing.
• Con: Full architectural benefit depends on already running Palo Alto NGFW infrastructure.

Cisco AI Defense

• Pro: Network-level enforcement with no required application code changes, easiest to deploy broadly across an org with scattered, uninventoried AI usage.
• Pro: Flexible SaaS or self-hosted deployment, appealing for regulated industries wary of routing AI traffic through a third-party cloud.
• Pro: Longest track record of the three, having launched in early 2025, roughly seven months ahead of Prisma AIRS 2.0 and about ten months ahead of Check Point’s Lakera integration.
• Con: Network-level inspection has less visibility into application-specific context than a code-level API call.
• Con: Robust Intelligence’s original brand and standalone product identity were fully absorbed, leaving less independent documentation of what changed versus the pre-acquisition product.

Expert Perspectives on the AI Security Guardrail Race

Executives at all three acquirers have been direct about why they moved. Nadav Zafrir, CEO of Check Point Software Technologies, framed the strategic logic behind the Lakera deal plainly: “AI is transforming every business process, but it also introduces new attack surfaces,” a statement made in Check Point’s official acquisition announcement. On the choice of target specifically, Zafrir added: “We chose Lakera because it brings AI-native security, superior precision, and speed at scale.”

Check Point’s press materials went further in describing the ambition behind the purchase, stating that “with this acquisition, Check Point sets a new standard in cyber security, becoming able to deliver a full end-to-end AI security stack designed to protect enterprises as they accelerate their AI journey,” a claim from the same official Check Point release.

Cisco has made a similar case for its own head start in the category. Reflecting on AI Defense’s first year, Cisco’s own AI security team wrote: “A year ago, we introduced the world to Cisco AI Defense, the industry’s first truly comprehensive enterprise AI security solution,” a line from Cisco’s blog post on expanding the platform for the agentic era.

Palo Alto Networks describes its own platform in more understated, architectural terms. Its technical documentation states simply that “Prisma AIRS is a purpose-built, centralized, comprehensive security platform,” a description drawn from Palo Alto’s AI Runtime Security documentation, emphasizing consolidation over the “first” or “industry-leading” framing its competitors have used.

Which Platform Should You Choose? Verdict and Recommendations

There is no universal winner in this comparison, because the three platforms optimize for different starting points. The decision should follow from what a buyer already has deployed, not from marketing claims about attack-technique counts or confidence-tier granularity.

• Choose Cisco AI Defense if your organization has scattered, uninventoried AI usage across many teams and wants network-level coverage without chasing down every application team to integrate an SDK, and if you already run Cisco networking or security infrastructure.
• Choose Prisma AIRS if you run Palo Alto NGFW hardware today and want the most complete module set (agent security, red teaming, model security) under a single contract, and you are comfortable with deeper lock-in to the Palo Alto ecosystem.
• Choose Check Point’s Lakera integration if your engineering team wants direct, granular API-level control over guardrail behavior per feature, and you are already a Check Point Infinity customer looking to consolidate AI security into that platform.
• Choose none of the three, for now, if your AI footprint is small (one or two internal chatbots, no autonomous agents with write access to production systems) and a lighter, open-source guardrail library meets your risk tolerance until your AI surface area grows.
• Run a shadow-mode bake-off if budget allows: because none of the three has an independent, third-party benchmark backing its detection claims, the only reliable way to compare real-world performance on your own traffic is to pilot more than one platform in monitor-only mode before committing to a multi-year enterprise contract.

The honest verdict, backed by the data available in September 2026, is that this market is too young for a single, clear-cut “best” answer. Cisco has the longest track record, Palo Alto has the broadest module set on paper, and Check Point has the most transparent, developer-facing API. All three are backed by companies with the balance sheets to keep investing, evidenced by continued acquisitions (Portkey for Palo Alto, Cyata/Cyclops/Rotate for Check Point) well after their initial AI security purchase closed. Buyers should treat this as an evolving category, re-evaluate at contract renewal, and demand a shadow-mode pilot rather than relying on vendor-reported capability claims alone.

Frequently Asked Questions

Is Lakera still a separate company from Check Point?
No. Check Point completed its acquisition of Lakera AI AG on October 22, 2025. Lakera operates today as a Check Point product line, keeping its original branding and website as of mid-2026, but it is legally and financially part of Check Point Software Technologies.

What happened to Protect AI’s original products, Recon, Radar, Guardian, and Layer?
Palo Alto Networks has not published documentation mapping those four legacy Protect AI product names directly onto Prisma AIRS 2.0’s current module structure (AI Agent Security, AI Red Teaming, AI Model Security). The underlying technology appears to have been absorbed into these three modules rather than kept as separately branded products.

How much do Lakera, Prisma AIRS, and Cisco AI Defense cost?
None of the three vendors publishes public pricing for these AI security platforms as of September 2026. All three are sold through custom enterprise quotes, typically bundled into a broader platform contract (Check Point Infinity, Palo Alto Prisma/Strata/Cortex, or Cisco Security Cloud).

Which platform has the best prompt-injection detection rate?
No independent, third-party benchmark comparing prompt-injection or jailbreak detection rates across these three platforms has been published as of September 2026. Any claim of a specific detection percentage for one vendor over another is vendor-reported, not independently verified.

Do I need an AI security platform if I only use one internal chatbot?
Probably not yet. These platforms are built for organizations running multiple AI features, autonomous agents, or third-party models across teams, where a single point of enforcement and monitoring saves more effort than it costs. A single, low-risk internal tool may be adequately covered by lighter-weight, open-source guardrail libraries.

Does Cisco AI Defense require changes to my application code?
Cisco markets AI Defense’s core value proposition as network-level enforcement that does not require agents or libraries inside application code, which differentiates it from Check Point’s more code-integrated Guard API approach. Some advanced Cisco AI Defense features may still benefit from deeper integration, but the baseline network-level coverage does not require it.

Can I run more than one of these platforms at the same time?
Technically yes, since they operate at different layers (network versus application), but doing so adds cost and operational complexity without a clear, published benefit. Most organizations are better served by piloting each candidate in shadow mode and standardizing on one primary platform.

Which acquisition happened first?
Cisco moved first, announcing intent to acquire Robust Intelligence on August 26, 2024, and closing on September 24, 2024. Palo Alto Networks followed, announcing intent to acquire Protect AI on April 28, 2025, and closing on July 22, 2025. Check Point was last, announcing its agreement to acquire Lakera on September 16, 2025, and closing on October 22, 2025.