SolonGate

Product Introduction

1. Definition: SolonGate is an infrastructure-level, zero-trust security gateway designed specifically for autonomous AI agents. It operates as a deterministic proxy that intercepts and governs all tool calls (e.g., API requests, database queries, system commands) attempted by Large Language Models (LLMs) like Claude or Gemini before they are executed on internal systems, APIs, or databases.
2. Core Value Proposition: SolonGate provides comprehensive action governance for AI agents, mitigating the critical security risks associated with their autonomous execution. It exists to block risky, unauthorized, or destructive actions in real-time, enabling secure deployment of AI in air-gapped, mission-critical, and enterprise environments by enforcing strict, auditable policies at the execution layer.

Main Features

1. Deterministic Policy Engine: This is the core rule-based filtering system. It reads the exact tool-call payload—including the action (e.g., "Bash," "Write," "WebFetch") and its arguments (e.g., "rm -rf /var," "cat ~/.aws/credentials")—and compares it against a predefined set of security policies. It makes a deterministic ALLOW or DENY decision in milliseconds, based solely on the content of the call, not probabilistic AI analysis. This ensures predictable, consistent enforcement of security rules.
2. Isolated AI Judge: This secondary, isolated AI component evaluates the semantic context and intent behind a tool call that may be flagged by the Policy Engine. It provides an additional layer of analysis to assess nuanced risk, such as data exfiltration attempts disguised as legitimate web fetches. Its judgments are logged alongside the Policy Engine's decision for full transparency.
3. Tamper-Evident Audit Log: Every single tool call intercepted by SolonGate is streamed in real-time to a secure, tamper-evident log. This log records the complete decision pipeline: the raw tool call, the verdict (ALLOW/DENY), the specific policy or rule applied, and the reasoning from the AI Judge. This provides an immutable audit trail for compliance, forensics, and operational monitoring of all AI agent activity.

Problems Solved

1. Pain Point: Preventing catastrophic errors and security breaches from autonomous AI agents. Traditional chatbot guardrails only filter input prompts, not the actual system-level actions agents are authorized to perform. A compromised or misaligned agent with tool access could execute destructive commands (e.g., rm -rf /), exfiltrate sensitive data (e.g., cat ~/.aws/credentials), or violate data residency laws.
2. Target Audience: Security Operations (SecOps) teams, DevOps engineers, platform architects, and compliance officers responsible for deploying and governing AI systems in enterprise, government, or cloud environments. Key personas include CISOs (Chief Information Security Officers) in regulated industries, MLOps engineers managing AI agent pipelines, and developers building with frameworks like Claude Code or OpenClaw.
3. Use Cases: Securing AI agents that have access to production databases, cloud infrastructure, or internal APIs; enforcing compliance (e.g., GDPR, HIPAA) by preventing unauthorized data access or movement; protecting air-gapped systems where AI agents perform maintenance or analysis tasks; providing auditable proof of AI action governance for regulatory requirements.

Unique Advantages

1. Differentiation: SolonGate operates at the execution layer, not the prompt layer. Unlike LLM guardrails, prompt filters, or safety classifiers that analyze the conversation, SolonGate intercepts the actual tool-call payload—the final, executable instruction sent to a system. This makes it resistant to prompt injection attacks that manipulate the AI's reasoning but cannot bypass the policy-enforced firewall on the action itself.
2. Key Innovation: The combination of a deterministic Policy Engine for immediate, rule-based filtering with an isolated AI Judge for contextual analysis creates a unique "verify then decide" architecture. This hybrid approach ensures low-latency enforcement of hard rules while leveraging AI for nuanced risk assessment, all within a zero-trust, air-gapped-compatible proxy model.

Frequently Asked Questions (FAQ)

1. How does SolonGate differ from LLM guardrails or prompt filters? SolonGate is an infrastructure-level execution firewall, while guardrails are prompt-level filters. Guardrails analyze the text conversation to block harmful prompts or outputs. SolonGate ignores the conversation and intercepts the final tool-call command (e.g., a SQL query, a shell command, an API call) attempted by the AI, blocking it before it reaches your internal systems. It provides security at the action layer, not the conversation layer.
2. Does SolonGate require outbound internet access? No, SolonGate is designed for deployment as an on-premise or air-gapped proxy. It requires no outbound internet connectivity to function, as all policy enforcement and auditing are handled locally. This makes it suitable for isolated, high-security environments where AI agents operate on internal networks.
3. How does the execution firewall impact system latency? The deterministic Policy Engine evaluates tool calls in milliseconds, adding negligible latency to the AI agent's workflow. This low-latency interception is crucial for maintaining responsive, autonomous agent operations while ensuring security.
4. How does SolonGate integrate with existing autonomous systems? SolonGate integrates as a transparent zero-trust proxy positioned between your AI agents (e.g., Claude Code) and your internal tools (MCP servers, APIs, databases). The agents are configured to point to SolonGate as their tool endpoint, which then forwards only the allowed actions to the actual backend services after inspection.
5. What happens when an unauthorized action is detected? When the Policy Engine or AI Judge determines an action is forbidden, SolonGate instantly blocks the tool call, preventing it from executing. The DENY event, along with the full context (the attempted command, the rule violated), is immediately streamed to the tamper-evident audit log for review and alerting. The AI agent receives a denial response, stopping the risky action.