Why AIGuard · AI security for growing teams

Discover every AI.
Protect every transaction. Control every agent.

AIGuard connects discovery, data protection, agent governance and runtime audit across supported SaaS AI, model APIs, MCP servers, tools and enterprise resources.

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Policy model across supported surfaces
DLP
Sensitive-data controls where enabled
MCP
Agent and tool governance
Audit
Evidence for investigations and reviews
The SMB problem

AI risk is spread across too many places.

Employees use SaaS AI. Applications call model APIs. Agents read files and call tools. AIGuard gives growing teams one practical policy layer across those surfaces.

Shadow AI becomes visible

Discover sanctioned and unsanctioned AI applications, assign risk, and enforce allow, warn, or block decisions.

App catalog · SWG · CASB

Sensitive data stays governed

Detect PII, secrets, source code, financial data, and confidential business content before it leaves through supported AI paths.

DLP · redaction · local inspection

Agents stay within bounds

Govern agent identity, MCP servers, tools, skills, file access, and high-risk actions with policy and approval workflows.

Least privilege · ATP · approvals

Every decision becomes evidence

Keep searchable policy events, session timelines, detection context, and exportable audit evidence for customers and reviews.

Audit trail · investigations · reports
How the layer works

AIGuard sits between AI activity
and the resources it can reach.

The same control layer connects discovery, data protection, risk and approvals across supported AI surfaces. It gives your team one place to make and prove policy decisions.

Where AI activity starts

Employees, applications and agents use these supported surfaces to send prompts, call models and initiate actions.

Where AIGuard makes the decision

Identity, data, action and risk context are evaluated against policy before supported activity proceeds. Decisions can allow, warn, block, redact, monitor or request approval.

What the activity can reach

Controls apply to the people, tools, files, databases, APIs and enterprise services connected to supported AI workflows.

AI surfaces
SaaS AIBrowser prompts and uploads
Model APIsGateway and managed AI traffic
Agents & MCPTools, skills and autonomous actions
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AIGuard control layer

One policy model evaluates identity, data, action and risk before supported activity proceeds.

DiscoverDLPRiskLeast privilegeApprovalAudit
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Connected resources
People & devicesUsers, teams and sessions
Tools & dataFiles, databases and business data
Systems & APIsApplications and enterprise services
What makes AIGuard different

One practical control layer
for the AI already in your business.

Start with the risk you have today. Add deeper enforcement as your AI footprint grows—without replacing the rest of your security stack.

01 · Add-on deployment

Improve security without redesigning your infrastructure

Start with browser-based coverage and add gateway or agent controls as needed. No blanket network redesign is required; deployment scope depends on the supported integration.

02 · One policy model

Protect supported AI transactions across the workflow

Apply consistent data, access and approval decisions across SaaS AI, model APIs, managed AI applications and agentic workflows where enabled.

03 · Unified visibility

See the people, apps and agents behind AI activity

Bring supported AI applications, users, agents, MCP servers, tools, resources and risk context into one operational view.

04 · Audit by design

Know what happened and why a decision was made

Keep searchable events, policy decisions, approvals and session timelines for supported activity, investigations and customer security reviews.

Platform capabilities

Controls that map to the way AI is used.

Use the capabilities that match your rollout. Coverage and enforcement depend on the integration and deployment path.

Agent Scout

Discover and inventory supported agents, identities, tools, MCP servers, models and connected resources.

AI Gateway / LLM Proxy

Route supported model traffic through a controlled gateway for policy, provider routing, key management and audit.

DLP

Detect sensitive data in supported prompts, responses, uploads and workflows, then allow, warn, block or redact.

Agentic Broker / MCP Gateway

Control supported agent-to-tool and agent-to-resource interactions using identity, policy, risk and approval context.

Risk Engine

Evaluate activity using user, agent, device, tenant, data, action and behavioural context.

Policy Bundles

Apply reusable allow, warn, block, redact, monitor and approval policies across the workflow.

Human Approval

Require review before sensitive, high-impact or irreversible actions where the integration supports it.

Audit

Provide searchable events, session timelines, policy decisions and investigation evidence.

Compared with common approaches

Built for the gaps between tools.

ApproachTypical blind spotWhere AIGuard can help
Traditional DLPRules built for files and structured channels, not prompts or agent actions.AI-aware content and supported tool-call policy.
Network-only controlsLimited visibility into local browsers, IDEs, and developer agents.Browser, API and supported agent/MCP coverage.
Agent-only securityDoes not govern employee use of consumer AI or shadow AI applications.One inventory and policy model across people and supported agents.
Manual policy documentsAdvice arrives after the user or agent has already acted.Supported inline enforcement, approval, and audit evidence.

Make AI safer to adopt.

See your AI app, data, and agent risk in a live walkthrough.

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