Looking Beyond Lakera? 6 Alternatives That Made Our Shortlist in 2026

Lakera Overview (Now Part of Check Point)

Lakera helped establish the market for dedicated security controls around generative AI applications. Its evolution under Check Point now gives security teams a broader platform context, but it also changes how buyers should assess architectural fit.

As an independent platform, Lakera focused on protecting generative AI applications through Lakera Guard, adversarial testing, and threat intelligence informed by its Gandalf red-teaming environment. Its runtime controls inspected prompts and model responses for risks such as prompt injection, data leakage, unsafe content, and policy violations.

Check Point announced the acquisition in September 2025 and completed it in the fourth quarter. Lakera now contributes to Check Point’s AI Defense Plane, which combines discovery, governance, observability, runtime control, and validation across employee AI tools, applications, and AI agents.

That expansion may suit organizations seeking AI security within a larger Check Point portfolio. Other teams are reassessing whether they need a gateway-centered control plane, broader lifecycle security, deeper application context, stricter in-org data handling, or execution evidence tied directly to production business outcomes.

What Moves Teams to Look Beyond Lakera

Lakera remains relevant for runtime guardrails and AI threat testing. Teams typically consider Lakera alternatives when their requirements extend beyond inspecting AI interactions at defined control points.

• Prompt-Layer Inspection Without Full Execution Context: Prompt, response, and selected tool-call inspection can detect important threats, but it may not reconstruct every internal service call, resource access, code path, identity, and resulting business action.
• Coverage Gaps for Agents That Operate Across Business-Critical Workflows: Lakera’s focus, like most of the market’s, is productivity and endpoint AI, i.e., the assistants and copilots employees use day to day. Custom-built enterprise agents are a different category. They’re written by your own engineers and wired directly into payment rails, customer records, claims systems, and infrastructure, where one action moves money or changes business state instead of producing text. Protecting them requires controls that follow the complete workflow, from the initiating identity through the resulting business action, not only the application-model exchange.
• Gateway-Based Architecture and Its Limits Inside the Application: A gateway sees traffic that passes through it. It does not automatically reveal how application code processes that traffic or what happens between internal components.
• In-Org Data Privacy Unverifiable by Default: Regulated organizations should verify where prompts, code, telemetry, traces, and policy decisions are processed and retained rather than assume that all execution data stays inside their environment.
• The Platform Consolidation Question After an Acquisition: Acquisition can improve integration and operational scale, but buyers should confirm roadmap continuity, licensing, deployment dependencies, and whether the combined platform still matches their original use case.

Lakera Top Alternatives: TL;DR

The strongest alternative depends on whether the tool’s priority is enterprise agent execution, broad AI posture, model security, network-level controls, or data governance.

The summaries above reflect the vendors’ current official product descriptions.

6 Lakera Alternatives for Enterprise Agent Security

These platforms approach AI security from materially different architectural positions. The comparison should therefore focus on the environment being protected, the execution and audit evidence teams need, and where enforcement occurs.

1. Rein Security

Rein Security is the only security platform purpose-built for enterprise agents: production systems connected to customer data, money, regulated workflows, and core business operations. Its code-native, in-process architecture provides full agent tracing across prompts, service calls, tool invocations, resources, identities, and resulting business outcomes.

The same zero-friction deployment covers inventory, posture, vulnerability management, compliance, supply chain security, governance, MCP protection, SCA and reachability, AI-powered SAST, API security, and detection and response. Business-aware guardrails operate on every agent action, while every byte of execution data stays inside the organization.

Pros

• Deterministic, execution-level visibility connected to business outcomes.
• Granular guardrails enforced inside the application flow.
• Complete in-org privacy without a vendor gateway.

Cons

• Designed primarily for enterprise agents, not employee productivity AI controls.
• Best suited to organizations with production applications and internal engineering teams.
• Using Rein to consolidate AI security and broader AppSec capabilities may require coordination across AI security, AppSec, and platform teams.

2. Noma Security

Noma Security provides a broad platform for discovering, governing, testing, and protecting AI systems and agents. Its portfolio includes AI security posture management, access control, adversarial testing, and AI detection and response across homegrown applications, SaaS agent platforms, coding assistants, and MCP servers.

This breadth can help security, data, and MLOps teams coordinate around a shared AI inventory and risk program. Public positioning emphasizes coverage across diverse AI environments, so buyers that specifically need code-native tracing of every production action should verify the depth of execution context and where telemetry is processed.

Pros

• Broad coverage across AI discovery, posture, testing, and runtime defense.
• Supports varied AI environments rather than a single model or application type.
• Useful for cross-functional AI governance and security programs.

Cons

• Broad scope may require multiple integrations and operating workflows.
• Public materials do not establish the same in-process business-outcome tracing as Rein.
• Teams with strict data sovereignty requirements should validate deployment and data paths.

3. Protect AI (Now part of Palo Alto Networks Prisma AIRS)

Protect AI is now part of Palo Alto Networks and contributes to Prisma AIRS. The platform covers AI model security, posture management, red teaming, runtime APIs and firewalls, data protection, and agent security, giving organizations a consolidated option across development and production.

Prisma AIRS is especially relevant for enterprises already standardizing on Palo Alto Networks. Its broad platform model can reduce point-tool fragmentation. However, API, firewall, and gateway controls should not be treated as equivalent to full in-process tracing of application behavior and business outcomes.

Pros

• Broad security coverage across models, applications, agents, and runtime.
• Strong fit for Palo Alto Networks platform consolidation.
• Combines model scanning, red teaming, posture, and runtime controls.

Cons

• Platform breadth may introduce licensing and operational complexity.
• Gateway or API inspection may not expose every internal execution step.
• Smaller teams may not need the full Palo Alto Networks ecosystem and accompanying complexity.

4. HiddenLayer

HiddenLayer offers AI discovery, model scanning, supply chain security, attack simulation, runtime security, guardrails, and agentic and MCP protection. Its model-focused capabilities are valuable for organizations importing third-party models or managing proprietary model artifacts that could contain malware, backdoors, unsafe code, or integrity issues.

The platform also extends into generative AI and agent runtime defense. Buyers should distinguish model and AI lifecycle protection from application-level execution tracing, especially when the requirement is to connect a specific agent action to the user, code path, resource, and business result that produced it.

Pros

• Strong model and AI supply chain risk scanning capabilities.
• Combines pre-deployment testing with runtime protection.
• Supports generative, predictive, and agentic AI environments.

Cons

• Model-centric strengths may not align with the needs of application-only teams.
• Public materials do not show equivalent business-outcome attribution.
• Deployment and data residency requirements require case-by-case validation.

5. Robust Intelligence (Now Cisco AI Defense)

Cisco completed its acquisition of Robust Intelligence in 2024 and used its technology as a foundation for Cisco AI Defense. The current platform combines AI application validation, runtime protection, model assessment, guardrails, and threat intelligence within Cisco’s networking and security portfolio.

Cisco explicitly emphasizes network-level visibility and enforcement without application libraries. That can simplify adoption for Cisco-aligned environments, but network telemetry cannot automatically provide the code-level semantics, internal execution chain, or business context available from an in-process architecture.

Pros

• Strong integration with Cisco networking and security infrastructure.
• Combines application validation with runtime AI controls.
• Benefits from Cisco Talos intelligence and enterprise operating scale.

Cons

• Network-level inspection may miss internal application semantics.
• Best fit may depend on existing Cisco architecture and procurement.
• Broader Cisco integration can be excessive for narrowly scoped AI projects.

6. Securiti AI

Securiti AI, acquired by Veeam in December 2025, centers on data security posture management, privacy, governance, access, and AI trust. Its AI Security and Governance capabilities discover models, classify risks, map data flows, monitor data use, and implement controls across cloud, SaaS, and internal environments.

This data-centric approach is well suited to organizations whose primary concerns are sensitive data, regulatory classification, privacy operations, and governance. It is less directly aligned with teams seeking deterministic, code-level reconstruction and enforcement across every action of a production enterprise agent.

Pros

• Strong data governance, privacy, DSPM, and compliance capabilities.
• Maps AI systems to data sources, risks, vendors, and obligations.
• Relevant to hybrid, multicloud, and SaaS data estates.

Cons

• Data-centric controls are not a substitute for full agent execution tracing.
• Less focused on application code and agentic supply chain reachability.
• Broad governance programs may require extensive data and system integration.

What We Looked For in Each Alternative

We looked at each Lakera alternative through the requirements security teams are likely to prioritize when protecting AI applications and agents in production. Because these products approach AI security from different architectural positions, we did not compare them solely by the number of features they offer.

Our comparison considered:

• Primary Security Focus: Whether the platform is centered on prompt and response inspection, AI posture management, model security, network-level protection, data governance, or production agent execution.
• Depth of Visibility: Whether it observes only selected AI interactions or can connect activity across prompts, tool calls, services, identities, resources, code paths, and resulting business actions.
• Enforcement Point: Whether controls operate through gateways, APIs, networks, platform integrations, or inside the application execution flow.
• Security Coverage: Whether the product addresses a narrow AI security requirement or extends across discovery, posture, testing, runtime protection, vulnerability management, supply chain security, compliance, and governance.
• Deployment and Data Handling: How the platform is introduced into the environment, where sensitive telemetry and execution data are processed, and whether organizations can keep that data within their own infrastructure.
• Enterprise Fit: The types of organizations, technology environments, and security programs for which each platform appears best suited.

We used these factors to identify each alternative’s main strengths, limitations, and likely fit. The comparison reflects differences in architecture and intended use rather than treating every platform as an interchangeable replacement for Lakera.

Lakera Alternatives at a Glance

The right fit depends on what the platform must protect, where it runs, what telemetry it captures, and where controls are applied.

Where Prompt-Layer Security Reaches Its Limits

Prompt controls remain important, but enterprise agents create risks through tools, identities, APIs, memory, code, and business actions. The OWASP Top 10 for Agentic Applications 2026 reflects this wider attack surface.

• What a Guardrail Sees vs. What an Agent Actually Does: A guardrail may approve a benign agent prompt while the agent might later chain tools in an unsafe sequence, use excessive privilege, or act on poisoned context.
• Internal Execution Context Changes the Security Equation: Prompt-layer controls generally cannot determine which identity initiated the run, which code executed, which resources were touched, or where policy enforcement should occur. That information comes from internal execution context.
• When Business Outcomes Become Part of the Attack Surface: A generated answer is one output. A refund, payment, claim decision, infrastructure change, or customer-account update changes business state and requires action-level control.

What Enterprise Teams Should Expect from a Lakera Alternative

The right requirements should follow the operational risk of the agents being protected. The NIST AI Agent Standards Initiative also highlights secure agent operation, interoperability, identity, and standards development.

1. Full Execution Tracing Beyond the Prompt and Response Layer: Capture prompts, service calls, tools, resources, identities, code paths, and outcomes.
2. Total Coverage Across Posture, Supply Chain, AppSec, and Governance: Avoid isolating agent runtime risk from code, dependencies, APIs, configuration, and compliance.
3. Business-Aware Guardrails Enforced at the Action Level: Evaluate whether each action is appropriate for the user, resource, workflow, and expected outcome.
4. Every Byte of Execution Data Staying Inside the Organization: Verify architecture, processing location, retention, and vendor access.
5. Single Deployment Across AI and Non-AI Environments: Prefer controls that can secure mixed application estates without separate operating models.
6. Audit Evidence for EU AI Act, ISO 42001, NIST AI RMF, and SOC 2: Produce attributable records of actual execution and policy decisions.

Before You Migrate: Architecture and Deployment Considerations

Organizations that decide to migrate from Lakera should begin with an architectural inventory, not a feature list. Teams need to understand existing enforcement points, data routes, policies, evidence requirements, and ownership before replacing controls.

Where Lakera’s Controls Lived and What Replaces Them

Map each current API, gateway, browser, application, red-teaming, and policy integration. Identify whether the replacement needs equivalent prompt controls, broader model security, data governance, or deeper in-process enforcement.

Gateways vs. Code-Native Sidecars: The Visibility Difference

A gateway observes exchanges routed through it. A code-native sidecar can associate agent activity with internal execution, identities, resources, and business results without making the tool a required intermediary for execution data.

Mapping Existing Prompt Policies to Execution-Level Enforcement

Do not discard useful prompt and content policies. Extend them into action policies that specify permitted tools, resources, identities, transaction limits, approval conditions, and responses to anomalous behavior.

Compliance and Audit Evidence Requirements Before You Switch

Define what auditors and incident responders must reconstruct. Required evidence may include the initiating identity, policy decision, full execution chain, affected data, external calls, resulting business action, and proof that records remained within approved boundaries.

Conclusion

Lakera’s integration into Check Point expands its reach, but it does not make its gateway-based architecture equivalent to platforms that provide in-process execution visibility. The right alternative depends on whether an organization primarily needs broad AI posture, model protection, network controls, data governance, or full execution-level security for production agents.

For enterprise agents connected to money, customers, regulated data, and business-critical workflows, prompt inspection alone is insufficient. These systems require complete execution visibility, broad security coverage, business-aware action guardrails, and complete in-org privacy. Rein is purpose-built for that class of agent.