See Every AI. Govern Every Interaction.

Shadow AI is the use of AI tools, models, and browser extensions by employees without security team approval or visibility. It includes public chatbots like ChatGPT, embedded AI features in SaaS apps, unsanctioned browser extensions, and locally-run models such as Ollama or LM Studio. The risk is that sensitive data — PII, source code, and intellectual property — leaves the organisation the moment it is pasted into an unsanctioned model, irreversibly.

KavachLab triangulates across four vantage points simultaneously: an endpoint agent that sees processes, installed apps and local model APIs; a browser extension that sees the actual prompts, uploads and generated output; network sensors that read DNS, proxy, TLS fingerprints (JA3/JA4) and SASE/CASB signals; and cloud and API connectors that read provider and identity logs. No single vantage sees all AI usage, so combining all four removes the structural blind spots — including off-network laptops and locally-run models.

No. KavachLab runs entirely customer-hosted: the whole data plane is deployed inside your own tenant, whether that is your cloud (AKS, EKS, GKE, OKE), on-premises, or air-gapped. Prompts, files, and monitoring data never transit or reside on vendor infrastructure. Content is field-level encrypted with keys you own in your own KMS or HSM, and analytics are metadata-first, meaning they never decrypt content.

Enforcement decisions are made locally on the device in under 10 milliseconds and never depend on a cloud round-trip. Because the decision is synchronous and local, enforcement works even when a laptop is off-VPN or fully offline. Policy is graduated rather than binary — a coaching nudge, a justification prompt, an approval request, redaction, or a hard block — so security does not become a productivity tax.

Legacy CASB, DLP, and SWG tools inspect network traffic and sanctioned SaaS, so they cannot see the content of an AI prompt, an off-network laptop, or a locally-run model, and they cannot detect AI-specific threats such as prompt injection, jailbreaks, or AI-mediated data exfiltration. KavachLab is AI-aware: it inspects the actual prompt and upload before it is sent, understands AI-specific risk patterns, and enforces inline at the point of interaction rather than at the network perimeter.

KavachLab covers public assistants including ChatGPT, Claude, Gemini, and Microsoft Copilot; enterprise AI services including Azure OpenAI, AWS Bedrock, and Google Vertex AI; AI features embedded inside SaaS applications; browser extensions; and locally-run open-weight models via runtimes such as Ollama and LM Studio. Coverage is behaviour-based rather than a fixed allowlist, so newly released tools are discovered without waiting for a signature update.

Yes. KavachLab produces framework-mapped reporting and DPIA artifacts across the EU AI Act, GDPR, ISO 27001, ISO 27701, SOC 2, NIST CSF, NIST AI RMF, UAE PDPL, Saudi PDPL, PCI DSS, and HIPAA. It maintains an immutable, tamper-evident (WORM) audit trail, supports retention, legal-hold, and right-to-erasure workflows, and every enforcement decision is explainable — showing the top contributing risk factors rather than an opaque score.

A scoped, monitor-only discovery pilot typically begins producing a live AI usage inventory within days of deploying the data plane into your tenant. Policies are authored as code and can be simulated in monitor mode against real traffic before any enforcement is activated, so teams can validate impact before switching from observation to control.