Areebi vs Lakera (Now Check Point AI Security)

Lakera emerged as one of the earliest dedicated LLM security tools, best known for Lakera Guard - a prompt injection firewall that intercepts and classifies adversarial inputs before they reach a language model. The product was real, the threat it addressed was real, and for organisations whose primary concern was prompt injection, Lakera provided genuine value.

But Lakera's scope was deliberately narrow. It was an LLM security tool, not an AI governance platform. It answered one question well - "Is this prompt an attack?" - while leaving every other governance question unanswered:

• Who is authorised to use which AI models for which purposes? (No policy engine)
• Should this interaction be blocked, masked, or escalated for approval? (Limited action choices)
• Why was this specific decision made, and can we prove it to a regulator? (Partial provenance)
• What did the AI system see at the time of a failure? (No incident replay)
• Which unsanctioned AI tools are employees using? (Limited shadow AI detection)
• What is our total AI model exposure and risk posture? (No model registry)
• Are AI outputs - not just inputs - being enforced against policy? (Input-only scanning)

These are not edge cases. They are the core capabilities that SOC 2, HIPAA, and EU AI Act auditors expect to see in any organisation's AI governance programme.

In September 2025, Check Point Software acquired Lakera for an undisclosed amount. Lakera had approximately 52 employees and an estimated $5.7M ARR at the time of acquisition - making it a technology acqui-hire rather than a standalone product bet.

This follows a pattern across the AI security market. In 2024–2025, seven major AI security startups were acquired by infrastructure vendors:

• Robust Intelligence → Cisco ($400M)
• Protect AI → Palo Alto Networks ($650–700M)
• CalypsoAI → F5 Networks ($180M)
• Prompt Security → SentinelOne ($250–300M)
• Pangea → CrowdStrike ($260M)
• Lakera → Check Point (undisclosed)
• Promptfoo → OpenAI (undisclosed)

The implication for customers is consistent across all seven acquisitions: the standalone product disappears into the acquirer's platform. Lakera is now "Check Point AI Security" - available as a module within the Check Point security stack, not as an independent product.

For existing Lakera customers, this means:

• Migration pressure. Check Point will sunset the standalone Lakera Guard product and migrate customers to the integrated offering - which requires Check Point infrastructure.
• Roadmap capture. Lakera's engineering team now serves Check Point's priorities. AI governance features compete with firewall, VPN, and threat prevention for roadmap attention.
• Pricing bundling. What was a focused, affordable tool becomes part of an enterprise platform with enterprise pricing and minimum commitments.

If you are evaluating Lakera today, you are actually evaluating Check Point AI Security - with all the ecosystem requirements, pricing complexity, and platform dependencies that entails. For an alternative that remains independent and purpose-built, see how Areebi compares.

The comparison between Areebi and Lakera is not a feature-for-feature contest - it is a category difference. Lakera was an LLM firewall. Areebi is an AI control plane. The distinction matters because it determines what problems you can solve.

What an LLM firewall does

An LLM firewall sits between users and models, inspecting inputs for known attack patterns - prompt injection, jailbreak attempts, and adversarial inputs. It is a defensive perimeter control, analogous to a web application firewall (WAF) for AI. Lakera did this well.

What an AI control plane does

An AI control plane governs the entire AI interaction lifecycle: who can access which models, what data can flow in and out, what actions are taken on policy violations, why decisions were made, and whether the organisation can prove compliance. It encompasses the firewall function but extends to policy, governance, audit, and compliance.

The practical difference:

Governance question	LLM firewall (Lakera)	AI control plane (Areebi)
Is this prompt an attack?	Yes	Yes
Does this prompt contain sensitive data?	Yes	Yes
Should this user access this model?	No	Yes - policy engine
What action should be taken? (block / mask / approve)	Limited	Granular, per-rule
Does the AI output violate policy?	No - input only	Yes - output enforcement
Is this AI making decisions or advising?	No	Yes - decision authority controls
Can we replay what the AI saw during an incident?	No	Yes - incident replay
Can we produce audit evidence for a regulator?	Logs only	Yes - compliance-mapped evidence
What unsanctioned AI tools are in use?	Limited	Yes - shadow AI discovery
What is our total model exposure?	No	Yes - model registry + risk scoring

Organisations that only need a prompt firewall may find Lakera's successor (Check Point AI Security) sufficient. Organisations that need to govern AI - to control, prove, and defend their AI usage - need a control plane. That is what Areebi provides.

The CTO's comparison data tells a clear story: of 14 governance capabilities evaluated, Lakera covers 4. Areebi covers all 14. Here are the capabilities that matter most - and that Lakera (now Check Point AI Security) cannot provide.

1. AI policy engine

Lakera had no concept of identity-aware, context-aware policies. It could not enforce rules like "Marketing can use Claude for copy, but not for customer data analysis" or "Contractors lose AI access outside business hours." Areebi's policy engine enforces these rules natively, with a visual builder that compliance teams can operate without engineering support.

2. Decision authority controls

As AI moves from advisory to autonomous, organisations must classify which AI interactions are "assist" (human decides) versus "decide" (AI acts). Lakera had no mechanism for this. Areebi enforces decision boundaries, ensuring AI does not silently escalate from recommendation to action - a growing source of regulatory liability.

3. Decision provenance

When an auditor asks "Why was this interaction blocked?", Lakera could show a detection log. Areebi provides the complete provenance chain: which policy was evaluated, what inputs were assessed, which rule triggered, what action was taken, and who approved the policy. This is the difference between a log entry and defensible evidence.

4. Incident replay

This capability is unique to Areebi. When an AI incident occurs, Areebi can reconstruct exactly what the model saw at the time of failure - the full prompt context, the policy state, the model version, the user's permissions. Standard logs capture events; incident replay captures the complete decision context. This is critical for forensic investigation and regulatory defence.

5. Model registry & risk scoring

Organisations cannot govern what they cannot see. Areebi's model registry catalogues every AI model in use - sanctioned and discovered - and assigns risk scores based on data sensitivity, deployment context, and compliance exposure. Lakera operated at the prompt level with no visibility into the broader model landscape.

6. Output enforcement

Lakera scanned inputs. Areebi scans inputs and outputs. This matters because sensitive data can appear in model responses - hallucinated PII, training data leakage, or outputs that violate content policies. Input-only monitoring misses an entire class of data exposure risks.

7. Audit-ready evidence

Lakera provided logs. Areebi provides evidence - pre-mapped to HIPAA, SOC 2, ISO 27001, NIST AI RMF, and EU AI Act control requirements. The difference between logs and evidence is the difference between "we have data" and "we can prove compliance." Regulators and auditors require the latter.

8. Governed AI workspace

Lakera was infrastructure - invisible to end users. Areebi includes a multi-model AI workspace with RAG, conversation history, and collaboration features. This is not a nice-to-have; it is the mechanism that drives adoption of governed AI channels. Without a workspace that employees actually prefer over consumer alternatives, governance controls are routinely bypassed.

Whether you are an existing Lakera customer facing migration to Check Point's platform, or evaluating alternatives before committing to the Check Point ecosystem, the transition to Areebi is straightforward.

What carries over

If you have Lakera Guard policies configured, the prompt-security rules translate directly to Areebi's input enforcement layer. Areebi supports the same detection categories - prompt injection, jailbreak, PII, PHI, PCI, secrets - plus custom patterns that Lakera did not support.

What you gain

Everything Lakera could not provide: a complete governance platform with policy engine, decision controls, incident replay, compliance automation, shadow AI detection, model registry, output enforcement, and a governed AI workspace. These capabilities activate alongside your existing prompt security rules - no gap in coverage.

Timeline

Phase	Duration	Activities
Assessment	1 week	Map existing Lakera rules to Areebi policies, identify governance gaps
Parallel deployment	1–2 weeks	Run Areebi in monitoring mode alongside existing Lakera/Check Point setup
Cutover	1 week	Activate enforcement, enable additional governance capabilities
Decommission	Ongoing	Remove Lakera/Check Point AI module after validation period

Total migration time: 3–4 weeks with no governance gaps during transition. Request a demo to see the migration path specific to your Lakera configuration, or take the free AI governance assessment to understand your current coverage gaps.

Lakera's original pricing was competitive - focused, affordable, per-API-call pricing for prompt security. Post-acquisition, the pricing model changes fundamentally.

Check Point AI Security (formerly Lakera)

Component	Estimated annual cost
Check Point platform (prerequisite)	$40,000–$100,000
AI Security module	$15,000–$35,000
Implementation services	$15,000–$30,000
Total (200 users)	$70,000–$165,000

Areebi (complete AI control plane)

Component	Annual cost
Areebi platform (200 seats)	$48,000–$84,000
Implementation	$5,000 (one-time)
Total Year 1	$53,000–$89,000

Areebi costs 25–46% less than Check Point AI Security while delivering 14 governance capabilities vs 4. And there is no prerequisite infrastructure - Areebi deploys standalone in your VPC, on-prem, or air-gapped environment. See transparent pricing on our website.