MLSecOps Platforms Compared: Protect AI (Now Palo Alto Prisma AIRS) vs HiddenLayer vs Cranium

A full-platform MLSecOps purchase is meant to span four connected capabilities rather than one: discovering every model, dataset, and AI component in use (including shadow AI nobody registered); scanning models and their supply chain for malicious code, unsafe deserialization, and provenance gaps before deployment; testing deployed models and applications against adversarial input; and monitoring and governing models at runtime with policy enforcement and audit-ready reporting. A team that only needs one of those four capabilities is usually better served by a narrower, cheaper tool built for that job specifically, which is why our scanner comparison and red-teaming comparison exist as separate articles. This one is for a team that has concluded it needs the broader program: a security or ML platform group responsible for the whole supply chain, likely reporting into a CISO or head of AI governance, evaluating a multi-year platform commitment rather than a single tool.

Treat every capability claim below as vendor-stated, confirmed against each company's own current marketing and documentation as of this writing, not as independently benchmarked. None of the three vendors' detection accuracy or format coverage has been verified by public third-party testing.

Protect AI, now inside Prisma AIRS. Protect AI built Guardian as a dedicated model-supply-chain security product before Palo Alto Networks announced its intent to acquire the company on April 28, 2025 and completed the deal on July 22, 2025 for $634.5 million in total consideration. That is the single most important fact for anyone evaluating this option today: Protect AI's technology is not purchased from an independent AI-security startup anymore. Palo Alto Networks has since folded that technology into Prisma AIRS, its broader AI security platform, which itself sits inside Palo Alto Networks' much larger security portfolio spanning network, cloud, and SOC products. Later Prisma AIRS releases extended the platform toward agent discovery, pre-deployment red-teaming, and runtime governance of AI agent tool calls, framed by Palo Alto Networks as securing the full AI lifecycle rather than just scanning model files. The practical result is that a team evaluating this option is not buying a dedicated MLSecOps startup's product; it is buying a module of a large, multi-domain security vendor's platform, priced and supported accordingly.

HiddenLayer. HiddenLayer organizes its platform around four named modules that map directly onto the four capabilities described above: AI Discovery (finding AI assets and shadow AI), AI Supply Chain Security (model scanning across a vendor-stated 35+ formats, covering malicious code, deserialization exploits, and architecture-embedded backdoors), AI Attack Simulation (continuous adversarial red-teaming), and AI Runtime Security, branded AIDR for AI Detection and Response, which HiddenLayer describes as non-invasive and agentless, requiring no access to a customer's model weights or proprietary data. HiddenLayer remains an independent, venture-backed company rather than part of a larger acquirer, which is the structural opposite of the Protect AI situation.

Cranium. Cranium's architecture is built around what the company calls the AI Trust Loop: Discover, Observe, Govern, Secure, Prove. Discovery centers on an AI Bill of Materials (AI-BOM) that inventories every model, dataset, agent, and third-party AI vendor relationship, including shadow AI. Observe layers on continuous monitoring, which Cranium describes as tracking more than 100 risk signals and 250-plus intent classifications. Govern maps that inventory against frameworks including NIST AI RMF, the EU AI Act, and ISO 42001. Secure adds adversarial testing and runtime threat defense, and Prove generates audit-ready documentation Cranium calls AI Cards and Trust Hubs. Cranium's own material and third-party trackers describe a 2026 acquisition of Aiceberg, an agentic-AI security and risk company, intended to extend Cranium's coverage toward AI agent behavior specifically. Relative to the other two, Cranium's architecture leads with governance and audit documentation first, with model scanning and runtime defense positioned as supporting capabilities rather than the platform's headline feature.

Adopting Protect AI's technology today means deploying it as part of Prisma AIRS, which in turn means adopting Palo Alto Networks' broader platform conventions. A team with no existing Palo Alto Networks footprint is not just licensing a model-security capability; it is opening a relationship with one of the largest security vendors in the market, with whatever account structure, renewal cycle, and cross-sell conversation that implies.

HiddenLayer describes its deployment as model-agnostic and agentless, positioned to deploy quickly across an existing model and application architecture without requiring access to model weights, prompts, or customer data. That framing is aimed specifically at organizations wary of a security vendor needing deep access to proprietary model internals to do its job.

Cranium's public site does not publish deployment-model detail (cloud-hosted, self-hosted, or hybrid), and this should be confirmed directly with Cranium during a sales conversation rather than assumed from marketing copy. Given the platform's governance and inventory focus, expect the deployment conversation to center on data-source integrations (model registries, CI/CD systems, vendor and procurement records) at least as much as on runtime traffic inspection.

Prisma AIRS's integration story is shaped by Palo Alto Networks' existing footprint: an organization already running Palo Alto Networks network security, cloud security, or SOC products should expect Prisma AIRS to integrate most naturally with that installed base, including whatever AI Gateway and agent-runtime capabilities Palo Alto Networks has layered on top of the acquired Protect AI technology. An organization with no other Palo Alto Networks products should confirm integration depth with its own actual ML tooling (registries, CI/CD, MLOps platforms) directly, rather than assuming continuity with what independent Protect AI once documented before the acquisition.

HiddenLayer's model scanning is described as integrating into CI/CD pipelines and MLOps platforms through lightweight containers, and as working with registries including Hugging Face, MLflow, SageMaker, and Databricks Unity Catalog, per HiddenLayer's own documentation. Confirm the current, complete integration list directly, since vendor integration catalogs change faster than published comparison content can track.

Cranium's integration surface is necessarily broader than a scanning tool's, since an accurate AI-BOM and lineage record depends on pulling data from model registries, cloud provider AI services, procurement and vendor-management systems, and potentially ticketing or GRC tooling to make its governance mapping useful. That breadth is also the integration risk: a governance platform is only as good as the completeness of what it can see, and gaps in any one data source degrade the accuracy of the inventory and audit documentation it produces.

Operating Prisma AIRS for this use case means operating it as part of a Palo Alto Networks platform, which suits a security team that already has staff trained on Palo Alto Networks tooling and wants fewer vendor relationships overall, at the cost of accepting that AI-specific priorities may compete for roadmap attention against Palo Alto Networks' much larger network and cloud security business.

HiddenLayer's four-module structure implies a smaller number of AI-specific dashboards to operate than stitching together separate scanning, red-teaming, and runtime tools from different vendors, though a team should still confirm during a trial how much day-to-day tuning (allow-listing legitimate but unusual models, triaging AIDR alerts) the platform actually requires versus what the marketing implies.

Cranium's operational model centers on governance workflows: reviewing and maintaining an accurate AI-BOM, keeping compliance mappings current as frameworks like the EU AI Act evolve, and using the platform's audit documentation output during actual audits or vendor risk reviews. That is a materially different operational rhythm than a scanning or runtime tool, closer to a GRC program than a security operations workflow, and it needs a team (or a clearly assigned owner) that treats governance upkeep as ongoing work rather than a one-time inventory exercise.

None of the three publishes per-seat, per-model, or tiered pricing for these capabilities. Protect AI's technology is quoted through Palo Alto Networks sales as part of a Prisma AIRS conversation, and there is no reason to assume today's pricing resembles whatever an independent Protect AI might have quoted before July 2025; the commercial context changed along with the ownership. HiddenLayer and Cranium are both quoted directly through their own sales processes, with no public list pricing for either company's platform-level offering as of this writing. For any of the three, get a written quote scoped specifically to the capabilities you actually need (model count, format coverage, number of monitored applications, governance framework mappings) rather than accepting a bundled platform number you cannot decompose.

Protect AI, now inside Prisma AIRS, strengths: the underlying model-supply-chain technology predates the acquisition and was built by a team that specialized in exactly this problem; post-acquisition, it inherits Palo Alto Networks' scale, support infrastructure, and (per Palo Alto Networks' own 2026 platform releases) a growing set of agent-runtime and red-teaming capabilities layered on top.

Protect AI, now inside Prisma AIRS, limitations: it is no longer a standalone purchase from a dedicated AI-security company; evaluating it means evaluating a module inside a much larger multi-domain security platform, with pricing, contract structure, and roadmap priorities set by Palo Alto Networks rather than by the original Protect AI team. A team that specifically wants a focused, independent MLSecOps vendor should treat this as ruled out by definition, regardless of the underlying technology's quality.

HiddenLayer strengths: a clean four-module structure that maps directly onto discovery, supply chain scanning, attack simulation, and runtime detection; a stated agentless, model-agnostic deployment approach that does not require access to proprietary weights; and continued independence as a company, which matters to a buyer specifically trying to avoid platform lock-in with a larger, non-AI-focused vendor.

HiddenLayer limitations: governance and compliance-framework mapping (Model Risk Context tying findings to OWASP, ATLAS, and NIST) is present but is not the platform's primary framing the way it is for Cranium, so a team whose main driver is audit-ready compliance documentation should confirm that capability meets its specific regulatory needs rather than assuming parity with a governance-first product.

Cranium strengths: the strongest of the three on AI asset inventory, model lineage, and audit-ready governance documentation, with explicit mapping to NIST AI RMF, the EU AI Act, and ISO 42001, plus a 2026 acquisition (Aiceberg) aimed at extending coverage to agentic AI behavior specifically.

Cranium limitations: its own public materials lead with governance and inventory rather than deep runtime traffic inspection or red-teaming depth, and deployment-model detail is not published, both of which need direct confirmation in a sales process. A team whose primary need is inline runtime blocking of adversarial model inputs may find Cranium a supporting layer rather than a replacement for a runtime-focused tool.

A security team that already runs Palo Alto Networks products for network, cloud, or SOC security, and wants AI security consolidated into that same vendor relationship rather than adding a new independent AI-security company to the vendor list, is the fit for Prisma AIRS. This suits an organization for whom fewer total vendor relationships outweighs the trade-off of AI-specific features competing for attention inside a much larger platform roadmap.

A team that wants a dedicated, independent AI-security platform with a clear four-module structure spanning discovery through runtime detection, and that is not otherwise anchored to Palo Alto Networks, is the fit for HiddenLayer. This suits an ML platform or security team that wants its AI security vendor's whole business focused on this one problem rather than being one product line inside a much larger portfolio.

A team whose most urgent driver is regulatory: preparing for EU AI Act obligations, building an audit-ready AI-BOM, or responding to a board or customer asking for AI governance evidence, is the fit for Cranium. This suits an organization where the immediate pain is not yet "we got attacked," but "we cannot currently answer what AI models and vendors we actually use, or prove it to an auditor."

None of these is a universal winner. A five-person ML platform team standardized on Palo Alto Networks elsewhere, an independent AI-native scale-up wary of platform lock-in, and a regulated enterprise racing an EU AI Act deadline are solving genuinely different problems even though all three could describe their need as "MLSecOps."

Skip all three full platforms, at least for now, if any of the following apply:

• Your only actual need today is catching malicious code in downloaded model files before they enter a pipeline. A full platform is more commitment than that problem requires; our Protect AI Guardian vs ModelScan vs JFrog comparison covers dedicated scanners, including a free open source option, that solve exactly that narrower problem without a platform-level purchase.
• Your only actual need today is pre-deployment adversarial testing of one specific LLM application: finding how it breaks under jailbreak and prompt injection attempts before it ships. Our HiddenLayer vs Lakera vs Prisma AIRS red-teaming comparison covers that category specifically, including products that do this well without the broader governance and inventory scope covered here.
• You have no defined ML model inventory process at all, not even a spreadsheet. A governance-heavy platform like Cranium depends on integrating with data sources that reflect an actual inventory; standing up even a manual inventory process first will make any platform evaluation more honest and may reveal you do not yet have enough models or vendors in production to justify a platform-level spend.
• Your organization has no budget or procurement appetite for a multi-year platform commitment, and no engineering or governance staff to operate one day to day. In that case, a narrower tool (a free scanner, a single red-teaming engagement, a manual compliance mapping exercise) is a more honest starting point than a platform purchase that risks becoming shelfware.

Run any evaluation against your own models, your own supply chain, and your own compliance obligations, not a vendor's demo environment or reference customer story.