Protect AI Alternatives: 5 Options Worth Considering in 2026

Protect AI addressed risks across machine learning models, pipelines, and applications. Following its acquisition by Palo Alto Networks, customers must now evaluate its technology as part of the broader Prisma AIRS portfolio rather than as an independent offering.

That does not make Protect AI unsuitable. Security teams should compare alternatives by the systems they must protect, the production context required, and their preferred vendor ecosystem.

Protect AI Overview, Now Part of Palo Alto Networks Prisma AIRS

Protect AI is no longer an independent vendor. Its technology now contributes to Prisma AIRS, so any current evaluation should consider both the original capabilities and Palo Alto Networks’ direction for the combined platform.

• What Protect AI covered as an independent platform: Protect AI developed model scanning, AI security posture management, AI red teaming, runtime protection, and open-source projects such as ModelScan. Its primary focus was the AI and machine learning lifecycle.
• What changed after the Palo Alto Networks acquisition in July 2025: Palo Alto Networks completed the acquisition on July 22, 2025. Protect AI’s capabilities are being incorporated into Prisma AIRS alongside AI agent security and other Palo Alto Networks controls.
• Where security teams are reassessing their options: Buyers may revisit vendor independence, licensing, deployment, data handling, integrations, and architectural fit. Existing customers should validate the current product roadmap rather than assume that every legacy workflow will continue unchanged.

Why Security Teams Look for Protect AI Alternatives

The reasons vary by organization. Some teams want comparable model and pipeline controls, while others need security for production agents acting across applications, tools, APIs, and business systems.

1. Model-Layer Coverage Without Enterprise Agent Execution Context: Model scanning may not satisfy teams that need to secure agent actions connected to code, resources, identity, and business outcomes.
2. Potential Platform Lock-In Risk After Acquisition Into a Larger Ecosystem: Consolidation may simplify procurement, but it can increase dependency on one vendor’s licensing, management plane, integrations, and roadmap.
3. ML Pipeline Focus That May Not Match Business-Critical Agent Workflow Requirements: Prisma AIRS has expanded beyond ML pipelines, but teams building enterprise agents should verify whether it provides the action-level tracing and policy enforcement their complete workflows require.
4. In-Org Data Privacy Requires Verification for Teams Outside Palo Alto’s Infrastructure: Organizations with data-sovereignty requirements should confirm where prompts, telemetry, findings, and support data are processed.
5. Potential Posture Management Gaps Across Code Security, Vulnerability Management, and API Security for Production Agents: Teams seeking one deployment for agent security, code security, vulnerability management, API security, and production context should verify how those capabilities work together.

Protect AI Alternatives: TL;DR

The five options featured here serve different security programs. The best fit depends on whether the priority is enterprise agent execution, model defense, AI governance, developer security, or offensive AI testing.

5 Best Protect AI Alternatives in 2026

Each alternative has a distinct architectural and operational emphasis. A proof of concept should test the product against representative models, agents, application paths, and compliance evidence requirements.

1. Rein Security

Rein Security is purpose-built for production enterprise agents operating in business-critical workflows. Its code-native sidecar sees the agent’s application layer runtime, providing in-process, execution-level visibility into prompts, tool calls, service calls, resources, and resulting business outcomes. The platform extends the same deployment across AI Agent Security and posture management workflows covering code security, vulnerability management, and related agent risks, applies business-aware guardrails to agent actions, and keeps execution data inside the organization rather than routing it through Rein-managed infrastructure.

Rein is for you if:

• Your agents move money, handle regulated data, or support customer-facing decisions.
• You need full agent tracing connected to identities, resources, code, and business outcomes.
• You want AI Agent Security and posture management across code and vulnerabilities through a single deployment.

Rein is not for you if:

• Your primary requirement is employee access control for third-party AI services.
• You only need isolated model-file scanning or periodic model red teaming.
• You are not building or operating production enterprise agents or applications.

What customers say: Dun & Bradstreet’s Jay DePaul says, “Rein sees what every agent does, and where it runs in real time.”

2. HiddenLayer

HiddenLayer provides an AI security platform organized around four modules: AI Discovery, AI Supply Chain Security, AI Attack Simulation, and AI Runtime Security. It can inventory models, agents, and AI workflows, assess model integrity before deployment, continuously simulate adversarial attacks, and detect threats affecting production AI systems. This combination makes HiddenLayer relevant to organizations seeking coordinated protection across model development, the AI supply chain, predeployment testing, and runtime operations.

HiddenLayer is for you if:

• Model integrity and adversarial machine learning are central concerns.
• You want continuous attack simulation alongside production threat detection.
• Your AI estate includes varied models, datasets, and deployment environments.

HiddenLayer is not for you if:

• Conventional SAST, SCA, and API security are the main buying requirements.
• Your decisive criterion is tracing agent actions to business outcomes in-process.
• You require one deployment spanning AI and non-AI application security.

What customers say: Doug Merritt highlights “robust, real-time protection against adversarial attacks” in HiddenLayer’s non-invasive approach.

3. Noma Security

Noma Security provides security and governance across AI applications, models, agents, data, and infrastructure. Its platform combines continuous discovery and AI security posture management with automated testing, red teaming, runtime protection, and compliance management. Noma can also identify agents, MCP servers, tools, and their relationships, then help organizations define access and action policies. This approach suits enterprises that need a contextual view of a growing AI estate across development and production.

Noma Security is for you if:

• You need discovery across a large and changing AI estate.
• Agent posture, MCP relationships, governance, and compliance are priorities.
• Several teams need a shared view of AI risk and remediation.

Noma Security is not for you if:

• Your program primarily needs developer-first open-source dependency scanning.
• Full code-level execution tracing is a central selection requirement.
• Your scope is limited to occasional testing of a small number of models.

What customers say: UiPath CISO Scott Roberts reports “dramatically increased visibility into all of our AI and ML infra, models, and their associated security and compliance risks.”

4. Snyk

Snyk is a developer security platform that identifies and helps remediate vulnerabilities in proprietary code, open-source dependencies, container images, and infrastructure as code. It integrates security checks into development environments such as IDEs, source code management systems, command-line workflows, and CI/CD pipelines. Snyk now also positions its platform around securing AI-generated code, governing development agents, and protecting AI-native applications, while retaining its established focus on developer workflows and software supply chain security.

Snyk is for you if:

• Code and open-source supply chain risk dominate the current backlog.
• Developers need findings and remediation guidance inside existing workflows.
• The organization wants consistent application security controls across many engineering teams.

Snyk is not for you if:

• Production agent action tracing is the primary security objective.
• You want a specialist centered primarily on model behavior testing.
• You require business-aware guardrails at each step of an agent’s execution.

What customers say: ICE/NYSE CISO Steve Pugh says its teams can “ensure we’re both shipping software faster as well as more securely.”

5. Mindgard

Mindgard provides attacker-aligned security testing and protection for AI models, agents, and applications. Its platform combines shadow AI discovery and reconnaissance, automated red teaming, security assessment, and runtime protection. Mindgard tests complete AI systems, including interactions among agents, tools, APIs, data sources, and workflows, rather than limiting assessments to isolated models. It is particularly relevant to teams seeking continuous adversarial testing that reflects how attackers identify, combine, and exploit weaknesses across an AI system.

Mindgard is for you if:

• Repeatable AI red teaming is the immediate requirement.
• You need evidence of how models and guardrails behave under adversarial testing.
• Offensive security teams want AI-specific testing integrated into their practice.

Mindgard is not for you if:

• Open-source dependency and conventional code scanning are the priority.
• You need complete in-process tracing from agent action to business outcome.
• Your goal is to consolidate agent security and broader security posture management under one deployment.

What customers say: A Fortune 500 bank red teamer says, “We’ve been able to significantly reduce the time spent on AI security assessments.”

What We Looked For in Each Alternative

The comparison considers each platform’s principal security layer, supported asset types, deployment approach, production controls, developer workflow coverage, data handling, and audit output. It also distinguishes model and pipeline security from AI Application Security for Custom-Built AI, including the AI Agent Security controls needed when software can select tools and act autonomously.

No single feature list proves operational fit. Teams should test how each product observes real execution, handles false positives, enforces policy, integrates with existing systems, and produces evidence that security and compliance reviewers can verify.

Protect AI Alternatives at a Glance: Comparison Overview

This condensed view shows the primary operating model of each alternative. Exact hosting, licensing, and integration options should be confirmed with each vendor.

The Gap Between Model Security and Enterprise Agent Security

Model security and agent security overlap, but they do not examine the same object. Current guidance reinforces the need to evaluate agent actions, permissions, tools, and deployment environments rather than treating the model as the complete system.

What Protect AI Covered at the Model and Pipeline Layer

Protect AI built strong capabilities around model files, ML supply chains, posture, red teaming, and runtime AI threats. Prisma AIRS now describes broader coverage across models, applications, and agents, so buyers should evaluate the current combined offering rather than the former platform alone.

What Enterprise Agents Require That Model Security Alone Cannot Provide

Model security addresses model artifacts, inputs, outputs, and adversarial behavior, but enterprise agents introduce additional risks through what they can access and do. The OWASP Top 10 for Agentic Applications 2026 demonstrates this broader security need through risks involving agent goals, tools, identities, privileges, memory, code execution, and multi-agent interactions. Enterprise agents, therefore, require visibility and control across their permissions, actions, connected systems, and outcomes. These are security requirements that model security cannot meet on its own.

Why Business Outcome Context Changes the Evaluation Entirely

The same technical action can be acceptable in one workflow and harmful in another. An agent action such as issuing a payment, approving a claim, or querying customer data must be evaluated against the initiating identity, permitted purpose, code path, resources touched, and resulting business outcome. This context-sensitive approach aligns with Gartner’s recommendation to distinguish agents by their autonomy level and scope of access rather than apply uniform governance.

What Enterprise Teams Should Expect From a Protect AI Alternative

Requirements should be written as evidence requests. The following checks are especially relevant when the alternative will protect production agents.

Before You Migrate: What to Evaluate Before Replacing Protect AI

A migration should begin with an inventory of current controls and dependencies. This prevents model-security functions from being lost while the organization adds broader agent or application coverage.

1. Map model scanning and supply chain coverage. Record repositories, model registries, pipelines, policies, exceptions, reports, and remediation workflows that the replacement must support.
2. Identify the primary risk layer. Separate model-file and pipeline risks from production agent actions, tool access, application vulnerabilities, and business-process abuse.
3. Decide between consolidation and specialist tools. Compare a single deployment covering agent security and posture management with a combination of specialist tools. Consider who will own and operate each approach, as well as the integration work it will require.
4. Define evidence requirements before switching. Specify retention, data location, audit mappings, export formats, access controls, and proof needed by security, legal, and compliance teams.

How to Switch from Protect AI to the Right Alternative

Start with a limited proof of concept that includes representative models, an agent workflow, and a conventional application path. Run the same test cases against each finalist, including a malicious model artifact, a prompt or goal manipulation attempt, excessive tool access, a vulnerable dependency, and an API misuse scenario.

Compare detection quality, context, policy enforcement, data flows, performance impact, integration effort, and audit output. Keep existing controls active until the replacement reproduces required coverage, then migrate workloads in phases with rollback criteria and named owners for unresolved gaps.

Conclusion

Protect AI’s integration into Prisma AIRS gives customers a broader Palo Alto Networks offering for AI security. Alternatives become relevant when an organization wants a different ecosystem, a more specialized model-testing approach, stronger developer workflow coverage, or security centered on enterprise agent execution.

Rein is the strongest fit when production enterprise agents affect revenue, regulated workflows, customer data, or other business-critical systems. Its sidecar sees the agent’s application layer runtime, connecting agent actions to execution and business context, supporting posture management across code and vulnerabilities, enforcing granular guardrails, and keeping execution data inside the organization.