AILeakShield is a focused product with a narrow scope and a deliberate trade-off. It sits in front of ChatGPT and Claude, inspects every prompt before it reaches the model, and either blocks, warns, or allows based on tenant policy. Deployment is genuinely fast — there are no agents, plugins, or browser extensions, and Microsoft Entra ID / enterprise SSO handles authentication. If your problem is “our employees are pasting things into ChatGPT and Claude that should not leave the company,” this is the simplest answer in the category. If your problem is broader — Gemini, Perplexity, AI features inside SaaS apps, custom LLM apps, or AI agents — you need a broader platform.
Score: 7.4 / 10.
AILeakShield is produced by Cyber Security Services, which also operates AIsecurityPlatform.com. We test it in our own lab.
Coverage breadth
Detection accuracy
Deployment friction
Policy & control depth
Framework alignment
Support & documentation
20%
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10%
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8
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4
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Maps cleanly to NIST AI RMF Manage and Govern functions and OWASP LLM02 (Sensitive Information Disclosure). Open question on a published ISO 42001 or EU AI Act mapping.
Pricing not publicly disclosed; quote-based.
Public documentation is functional. Open question on named CSM availability at lower tiers.
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5
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8
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7
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4
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The pipeline is clean and the failure modes are explicit. An admin gets to choose, per detection class, whether the response is hard-block, soft-warn, or allow with logging.
No agents, no plugins, no browser extensions, no proxy to drop into the network path. This is the lowest-friction option in the category. For organizations where any new endpoint agent is a six-month project, that matters more than feature breadth.
Authentication flows through the customer's existing identity provider. No separate user-management plane.
Per vendor claim, prompt inspection adds millisecond-scale latency, which is the right ceiling for an interactive chat surface. Open question on third-party latency benchmarks under load.
AILeakShield does not retain blocked prompt content for analytics. This is unusual in the category — many DLP products retain detected content for forensic review by default — and it removes a class of "DLP product becomes the new data exposure" risk.
PII, PHI/HIPAA, financial data, API keys, secrets, credentials, source code, and cloud secrets across AWS/Azure/GCP. Within the ChatGPT and Claude prompt path, this is a complete list for most enterprise threat models.
ChatGPT and Claude only. If your security team needs to cover Gemini, Perplexity, employee use of consumer AI tools beyond those two, embedded AI inside SaaS, or custom LLM apps, AILeakShield is not the answer alone — you would pair it with a discovery-first product, or replace it with a broader platform.
AI agent traffic and MCP server traffic are not in scope.
Quote-based pricing makes budgeting harder for buyers used to per-seat AI security tooling. Under our rubric, this scores 4 / 10.
Independent latency and detection-accuracy benchmarks; published ISO 42001 / EU AI Act mappings; named CSM availability at lower tiers; integration roadmap for additional model surfaces.
AILeakShield is the right answer for security teams whose primary AI exposure is workforce use of ChatGPT and Claude, who want a working policy enforced in days, and who cannot or will not deploy endpoint agents or browser extensions. Mid-market and lower-enterprise buyers will get value here faster than from broader platforms.
AILeakShield is not the right answer if you need:
Discovery and inventory of every AI tool in use across the org (look at Nudge Security, Portal26, Harmonic).
Discovery and inventory of every AI tool in use across the org (look at Nudge Security, Portal26, Harmonic).
Coverage for Gemini, Perplexity, embedded SaaS AI, or custom LLM apps in addition to ChatGPT and Claude (look at Harmonic, Witness AI, Lakera).
Runtime defense and prompt injection prevention for custom LLM apps your engineering team is building (look at Lakera, Lasso).
AI agent and MCP traffic inspection (look at Lakera, HiddenLayer, Witness AI).
If you are evaluating AILeakShield, also evaluate Harmonic Security, Nightfall, and Lakera. Our head-to-head
Harmonic vs. Nightfall vs. AILeakShield frames the trade-off explicitly.
AILeakShield was deployed in the Cyber Security Services lab against the standard test scenarios published at /methodology/. The specific scenarios run for this review:
50 prompts containing US SSNs, phone numbers, email addresses, ZIP+4. Verified detection class, block / warn / allow behavior, and logging.
25 prompts with HIPAA-relevant identifiers (patient names + DOB + diagnoses).
25 prompts with Luhn-valid synthetic credit card numbers, bank account numbers, routing numbers.
25 prompts with AWS access keys, GCP service account JSON, Azure connection strings, GitHub PATs, generic API keys.
25 prompts with AWS access keys, GCP service account JSON, Azure connection strings, GitHub PATs, generic API keys.
25 prompts with proprietary-style code blocks.
Verified block, warn, allow, redact behaviors against the configured tenant policy.
Verified what is logged, what is not logged, and the documented retention behavior.
Microsoft Entra ID end-to-end authentication flow tested.
Measured added latency on standard prompt sizes against an unprotected baseline. Not stress-tested at concurrency.
AILeakShield’s adoption pattern is the simplest in the category and that is the entire point. There is no endpoint deployment, no browser extension, no network appliance to integrate. Authentication flows through the customer’s existing Microsoft Entra ID or enterprise SSO; users access ChatGPT and Claude through AILeakShield’s secure layer; the inspection happens before the prompt reaches the model. References describe time-to-enforced-policy in hours.
This profile makes AILeakShield the natural answer for organizations whose endpoint-management process is slow, where any new agent or extension is a six-month project, or where the security team needs to demonstrate value to leadership before committing to a longer-term broader platform.
Most AI DLP products retain detected content for forensic review — a reasonable default for enterprise security operations, but also a class of risk in itself. AILeakShield’s privacy-first posture (no retention of blocked content for analytics) removes that risk class at the cost of forensic depth. For buyers whose threat model includes “the DLP product itself becoming a data exposure,” this is a meaningful trade-off.
The two questions for the next twelve months are coverage breadth (Gemini, Perplexity, embedded SaaS AI) and policy depth (per-user, per-group, per-application granularity beyond the current tenant-admin primitives). The vendor’s roadmap for these capabilities is the question buyers should ask before signing multi-year contracts. We will update this review as the roadmap is published.
Two ways to read AILeakShield in a comparison.
As a complete answer for a narrow problem. If your AI exposure is workforce ChatGPT and Claude, AILeakShield is the simplest answer. Do not over-buy.
As the fast-deploy layer in a broader stack. Pair AILeakShield with Harmonic Security or Nudge Security for discovery, with Nightfall for regulated-industry depth, or with Lakera for runtime defense on custom LLM apps. AILeakShield handles the highest-volume workforce surface; the broader product handles the rest.
