SOC 2 Type II Compliance & Trust Services

When enterprise organizations deploy generative AI, maintaining SOC 2 Type II compliance becomes a significant challenge. Transmitting data to external LLM providers expands your audit scope and introduces third-party vendor risk.

Panovista is engineered to help B2B organizations maintain and continuously enforce the AICPA’s five Trust Services Criteria (TSCs) by acting as an inline security boundary between your internal systems and external AI models.

1. Security (Common Criteria)

The Security criteria mandate that systems and data are protected against unauthorized access and exfiltration.

Panovista enforces zero-trust data flow at Layer 7. Even if an AI agent is compromised via a malicious prompt injection, Panovista’s deterministic proxy rules prevent the agent from exfiltrating system data to an unauthorized third-party endpoint.

Furthermore, Panovista satisfies the continuous monitoring requirements under the Common Criteria (CC) by generating cryptographically signed, tamper-evident logs for every single Model Context Protocol (MCP) tool execution.

[AI Agent] ──► [Panovista Proxy] ──(Generates HMAC-SHA256 Log)──► [Enterprise SIEM]

2. Confidentiality

The Confidentiality criteria require that restricted information (such as intellectual property, internal financial reports, or proprietary code) is protected throughout its lifecycle.

By deploying Panovista as a local sidecar, you can configure semantic redaction schemas that automatically catch and tokenize internal IP before it leaves your network. This allows your developers to use public LLMs for code assistance and reasoning without accidentally leaking confidential trade secrets into the model provider’s training corpus.

3. Privacy

The Privacy criteria evaluate how personally identifiable information (PII) is collected, used, and retained. When your AI agents interface with customer support databases or HR systems, Panovista acts as an automated anonymization gateway.

Privacy Enforcement Schema Example

You can mathematically guarantee privacy compliance by mounting declarative masking rules directly to the proxy:

{
  "version": "1.0",
  "policy_name": "soc2_privacy_enforcement",
  "rules": [
    {
      "field": "customer_email",
      "type": "regex",
      "pattern": "^[a-zA-Z0-9_.+-]+@[a-zA-Z0-9-]+\\.[a-zA-Z0-9-.]+$",
      "replacement_token": "[PII_EMAIL_REDACTED]"
    }
  ],
  "retention": "zero_state"
}

Because Panovista replaces PII with irreversible, zero-knowledge tokens in volatile memory, it ensures your downstream AI architecture complies with the AICPA’s Generally Accepted Privacy Principles (GAPP).

4. Processing Integrity

Processing Integrity ensures that systems perform their intended functions without error, delay, or accidental manipulation.

Because Panovista utilizes Format-Preserving Tokenization (FPT), the data sent to the external LLM retains its exact structural integrity (e.g., preserving the character length and formatting of a financial ledger or JSON payload). This guarantees that the AI model can still logically reason over the data and return an accurate, highly valid output back to your application without ever seeing the raw plaintext.

5. Availability

The Availability criteria ensure that systems are consistently accessible for operation and use.

Panovista is built on a highly concurrent, memory-safe Go architecture. It operates entirely as a stateless proxy, meaning it holds no persistent storage or database dependencies. This allows infrastructure teams to seamlessly horizontally auto-scale Panovista instances within Kubernetes (via standard ReplicaSets or HPA), ensuring massive throughput and 99.99% uptime for enterprise AI agent workloads.