A complete AI security platform for AI agents, MCP, and the full AI lifecycle
of enterprises are exploring AI applications
of enterprises unprepared for AI security challenges
of enterprises investing in AI security in 2025
MCP gives agents direct access to tools and resources without proper authorization or guardrails.
Prompt injections can trick agents into rogue actions, escalate privileges, and expose sensitive data.
Agents act at machine speed, allowing simple mistakes to cascade across systems.
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platform
Securing autonomous agents requires a comprehensive approach that spans the entire AI lifecycle—from discovery and hardening to runtime protection, governance, and compliance.
PointGuard AI is an enterprise AI security platform that delivers comprehensive protection for AI agents, MCP infrastructure, models, and data through the Agent Mission Control, the zero-trust MCP Security Gateway, integrated Runtime Guardrails, centralized policy enforcement, identity-aware controls, and continuous oversight across the enterprise.
agentic ai
AI agents interact directly with enterprise tools, APIs, and data through the Model Context Protocol. The Agent Mission Control and MCP Security Gateway provide a centralized control plane that governs how agents are identified and how they access these resources. With zero-trust authorization, granular tool permissions, and secure secrets management, organizations can safely deploy autonomous agents while maintaining security, visibility, and policy enforcement across MCP integrations.
Establish verifiable agent identity across every interaction
Enforce zero-trust authorization and MCP permissions
Gain full observability into agent behavior and anomalies
visibility
Visibility is the first step to securing agentic AI. Continuous discovery identifies every AI project, model, dataset, agent, MCP server, and endpoint across your enterprise, regardless of where they operate. By exposing shadow agents and unmanaged resources, security teams gain the clarity needed to manage risk, enforce governance, and scale agentic AI safely.
Inventory AI agents, models, datasets, and MCP resources
Detect risky or unapproved models, agents, and tools
Eliminate agentic blind spots with continuous discovery
hardening
AI agents behave unpredictably, which makes continuous adversarial testing essential. Adaptive red teaming simulates real-world attacks against agents, models, MCP integrations, and tool workflows to uncover weaknesses before deployment. Security posture management then hardens agentic environments by identifying misconfigurations, blocking malicious components, and enforcing best practices across the AI stack.
Simulate attacks against AI agents, models, and MCP tools
Detect prompt injection, jailbreaks, and unsafe behaviors
Identify misconfigurations and harden AI environments
guardrails
Agentic AI delivers value through continuous interaction with enterprise systems, APIs, and data pipelines. Intelligent guardrails enforce security policies across agent prompts, responses, and tool interactions in real time. Built-in DLP protection inspects content for sensitive data, detecting malicious inputs, unsafe outputs, and policy violations before agents execute actions.
Inspect agent prompts, responses, and tool interactions
Enforce policies in real time across agent actions and tool use
Prevent data leaks by automatically blocking, masking, or redacting sensitive data
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integrity
Agentic systems rely on complex supply chains of MCP servers, tools, models, libraries, and third-party services. Every dependency introduces potential risk. The platform continuously monitors these ecosystems while the Trusted MCP Knowledge Base evaluates and rates thousands of MCP servers and tools, helping security teams identify risky components before agents interact with them.
Monitor agent dependencies, MCP servers, and integrations
Correlate risks across applications, models, and infrastructure
Evaluate MCP tools using the Trusted MCP Knowledge Base
FAQ
Answers to common questions about AI Security, Agentic AI and the PointGuard AI platform.
An AI security platform protects the AI an organization builds and uses, including AI agents, models, applications, and data. It typically combines discovery of AI assets, security testing, posture management, runtime guardrails, and governance in one place. PointGuard AI is an enterprise AI security platform built for agentic AI.
PointGuard AI covers the full AI lifecycle in one platform, from discovering AI agents and models to testing, runtime protection, and governance. Instead of separate point tools, teams get one place to see and control AI agents, MCP connections, models, and data. PointGuard AI has been recognized by Gartner as a 2026 Coolest Vendor Innovation in AI Software Security.
Agentic AI security protects autonomous AI agents that act on their own across enterprise tools, APIs, and data. It covers verifiable agent identity, zero-trust authorization for the tools agents use, real-time guardrails on prompts and actions, and continuous monitoring of agent behavior. Without it, a prompt injection or a small agent error can spread across systems at machine speed.
Autonomous AI needs controls for identity, access, behavior, and oversight. In practice, that means giving every agent a verifiable identity, limiting each agent to the tools and data it needs, inspecting prompts and actions in real time, and keeping a full record of what agents do. PointGuard AI’s Agent Mission Control brings these controls together in one place.
MCP servers give AI agents direct access to enterprise tools and resources, so each connection needs authorization and oversight. Securing them means placing a zero-trust gateway between agents and MCP servers, granting granular tool permissions, managing secrets securely, and evaluating third-party MCP servers for risk before agents use them. PointGuard AI’s MCP Security Gateway provides these controls.
AI guardrails are policies that check what goes into and comes out of AI models and agents, and block anything unsafe. They can stop prompt injection, prevent sensitive data from leaking, and keep agents within approved actions. PointGuard AI’s Runtime Guardrails apply these policies automatically across AI applications and agents.
AI runtime security protects AI applications and agents while they are running, not just before deployment. It monitors live prompts, responses, and agent actions, and blocks, masks, or redacts risky activity in real time. This catches threats that only appear in production, such as malicious inputs or an agent misusing a tool.
AI security posture management (AI-SPM) continuously finds and fixes security weaknesses across an organization’s AI models, agents, and infrastructure. It identifies misconfigurations, risky models, and exposed data, then prioritizes what to fix first. PointGuard AI’s AI Security Posture Management correlates these risks across applications, models, and infrastructure.
AI supply chain security protects the third-party models, datasets, libraries, and MCP servers that AI systems depend on. An AI-BOM (AI bill of materials) is an inventory of those components, so teams know exactly what their AI is built from and can spot risky or vulnerable parts. PointGuard AI’s AI Supply Chain Security helps teams track and assess these components.
PointGuard AI gives teams one place to set AI policies, enforce them, and prove compliance. It inventories AI usage, applies policies across agents and models, and keeps audit records that map to AI regulations and industry frameworks. This helps security, risk, and compliance teams adopt AI with confidence.
Our expert team can assess your needs, show you a live demo, and recommend a solution that will save you time and money.
Market leaders from Gartner have included PointGuard AI in multiple reports on the emerging AI security space.
