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A.I.G (AI Infra Guard) vs Pylar: Features, Pricing & Which Is Better (2026)

A side-by-side comparison of A.I.G (AI Infra Guard) and Pylar — features, pricing, and ideal use cases — to help you decide which AI tool fits your workflow.

A.I.G (AI Infra Guard) logo

A.I.G (AI Infra Guard)

Tencent Zhuque Lab

Free

Tencent's open-source AI red teaming platform for scanning agents, agent skills, MCP servers and AI infrastructure, plus LLM jailbreak evaluation.

Key features

  • Agent Skills Scan: Audits agent skill packages against a nine-category risk taxonomy aligned with the public SkillTrustBench T01-T09 classification, including detection of .pyc bytecode bypasses and charset smuggling.
  • MCP Server Scan: Inspects MCP servers for threats such as tool poisoning, credential exfiltration and command injection, with tool whitelisting to prevent remote code execution during dynamic scanning.
  • AI Infrastructure Vulnerability Scanning: Checks deployed AI components against a library that has grown to roughly 130 components and over 2,000 CVE rules, covering frameworks such as llama.cpp.
  • Jailbreak Evaluation: Runs single-turn jailbreak operators plus multi-turn attack techniques including Many-Shot, PAIR, GOAT and ActorAttack to measure a model's resistance.
  • Agent Scan with OWASP Coverage: Assesses running agents using OWASP-derived skills and web exfiltration detection, with a dedicated agent red team skill for comprehensive assessment.
  • Standalone Scanner CLIs: skill-scan, mcp-scan and agent-scan each install as an independent command-line tool so scans can be wired directly into enterprise CI/CD pipelines.
  • Docker Deployment with Web UI: Deploy the full platform with Docker on 4GB+ RAM and reach the web interface at localhost:8088, or use a one-click install script or a source build.
  • AI Security Skill Market: A catalog of official security scanning skills, with the frontend fully open-sourced and integration available from OpenClaw chat via the aig-scanner skill.

Best for

  • Pre-Deployment Agent Audit: Scan an internally built agent and its skill bundle for prompt injection, exfiltration and privilege risks before it is released to staff.
  • MCP Supply Chain Review: Vet third-party MCP servers for tool poisoning and credential exfiltration before connecting them to production assistants.
  • CI/CD Security Gate: Run skill-scan as a standalone CLI on every pull request so risky agent skills fail the build rather than shipping.
  • Model Safety Benchmarking: Measure how a deployed LLM holds up against single and multi-turn jailbreak techniques before and after guardrail changes.
  • AI Infrastructure Patch Triage: Inventory AI serving components and match them against the CVE rule library to prioritise which hosts need patching.
  • Security Research and Reporting: Use the open scan engines and SkillTrustBench alignment as a reproducible basis for internal or published AI security research.
View A.I.G (AI Infra Guard) details
Pylar logo

Pylar

Pylar

Paid

Governed data access layer that lets AI agents query controlled SQL views and MCP tools without exposing raw databases.

Key features

  • Governed SQL Views: Create and manage curated SQL views that expose only authorized subsets or transformations of underlying tables, preventing agents from accessing raw database rows or schemas directly.
  • MCP Tool Publishing: Package governed views and query endpoints as MCP tools that can be published and deployed to any agent builder, simplifying distribution of controlled data capabilities to agents.
  • Fine-Grained Access Control: Enforce policies and permissions at the view or tool level so different agents or agent roles can only run allowed queries and receive permitted fields.
  • Secure Query Execution: Route agent queries through a managed execution layer that sanitizes inputs, applies limits and quotas, and prevents unauthorized SQL execution patterns.
  • Auditing and Logging: Capture detailed logs of agent queries and access events for compliance, forensics, and monitoring of data usage by agents and tools.
  • Database Integrations and Connectors: Connect to existing relational data stores and map schemas into governed views, enabling rapid adoption without migrating source data.
  • Create governed SQL views for safe data access
  • AI-powered MCP tool creation
  • Connect 100+ business tools and databases
  • Managed ingestion, ETL, and hosted warehouse
  • Cross-database joins and multi-database integration
  • Publish and deploy tools to any agent builder
  • Built-in observability and control pane
  • Create governed SQL views to expose controlled subsets of structured data to agents
  • Build MCP-compatible tools that can be deployed into agent builders
  • Deployable to any agent builder / agent framework (platform-agnostic integration)
  • Secure, scalable access controls for agent-driven queries against databases
  • Governance and policy enforcement for data access in agent workflows
  • GitHub presence for project assets and workstation tooling (PylarAI organization)

Best for

  • Safe Agent Access to CRM Data: Expose a limited, governed view of a customer database so conversational agents can answer customer-specific questions without full DB access or PII exposure.
  • Publishable MCP Tools for Agent Platforms: Package analytics or lookup queries as MCP tools and deploy them to multiple agent builders so agents can access standardized data functions.
  • Compliance-Focused Data Access: Maintain audit trails and enforce view-level permissions for regulated environments (finance, healthcare) where agent queries must be restricted and logged.
  • Operational Dashboards for Agents: Provide agents with curated operational metrics and KPIs from production databases without risking query patterns that could impact performance or reveal sensitive schema.
  • Multi-tenant SaaS Data Isolation: Create per-tenant governed views so agents serving different customers can query only their tenant data while using the same underlying infrastructure.
  • Prototype and Test Agent Workflows: Rapidly define safe SQL views to let agents prototype data-driven workflows without waiting for heavy engineering changes or database refactors.
  • Let AI agents query CRM, billing, and product data without direct DB access
  • Build and deploy MCP tools for customer support or sales assistants
  • Provide governed data access for agent-driven analytics and reporting
  • Host synced business data in a managed warehouse for secure agent usage
  • Provide AI/agent workflows safe, governed query access to enterprise SQL databases
  • Expose tightly scoped, auditable data views to third-party or internal agents
  • Build and deploy MCP connector tools for multi-agent platforms and agent builders
  • Enable controlled retrieval for retrieval-augmented-generation (RAG) systems using SQL-backed knowledge sources
  • Operationalize data access governance for agent-based automation and assistants
View Pylar details