Stop data exfiltration and pass compliance audits before they freeze your AI initiatives. Panovista is a stateless, zero-knowledge Layer 7 security gateway that drops directly in front of your internal database, file, and API tools.
When autonomous AI agents or developer IDEs invoke tools via the Model Context Protocol (MCP), raw JSON-RPC text strings stream out of your perimeter. Without an inline firewall, sensitive database records, internal API tokens, and customer PII are sent unprotected to third-party LLMs.
Malicious or hallucinated prompt payloads can manipulate your AI agents into calling internal backend tools with unauthorized data parameters, leading to silent data leaks.
Standard MCP setups lack automated Data Loss Prevention (DLP). Every raw customer email, financial table, and secret credential hits the external model wire unprotected.
Under Article 12 of the EU AI Act, high-risk AI deployments must maintain automated, unalterable event logs. Failing to prove what data your agents accessed carries severe structural fines.
Panovista operates as an inline network proxy. It speaks fluent JSON-RPC 2.0, intercepting agent traffic in-flight to protect your internal enterprise infrastructure.
Our lock-free Go engine compiles expressions directly into execution loops. Panovista catches PII, caches the raw values locally inside volatile container memory, and forwards sanitized placeholder tokens to the LLM. The true data is reverse-injected only on the secure return path back to the user's screen.
Panovista pipes clean logs directly to standard out (`stdout`), keeping our proxy 100% stateless. Every log entry is cryptographically sealed using an **HMAC-SHA256 signature** signed by an encryption key managed entirely within your local perimeter.
We compile Panovista directly into machine binary, discarding all OS operating layers to build a strict `scratch` image. This gives your CISO a deterministic, zero-vulnerability report on standard Snyk and CodeQL corporate scans, eliminating onboarding delays.
Consuming less than 20MB of memory, Panovista scales via the Sidecar Pattern. Rather than managing complex routing clusters, deploy identical instances of our container directly in front of individual database or file tools using our simple orchestration templates.
Enter your corporate parameters to receive our deployment spec sheet, validated Snyk/CodeQL reports, and an evaluation key for our private container registry.
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