Evaluate AIGuard on one AI workflow in two weeks Schedule a Demo

AI Runtime Security

Runtime enforcement for production AI systems.

Evaluate prompts, browser actions, MCP/tool calls, and outputs before the action executes.

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  • Prompt and output policy
  • MCP/tool authorization
  • Tenant risk thresholds
  • Evidence pipeline
AIGuard runtime security dashboard showing MCP scanner risk, tenant scope, connector health, and top risks.

Product Demo

See runtime decisions, inventory, and evidence.

Live dashboard views for MCP risk, AI inventory, and governance evidence.

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Animated AIGuard demo showing runtime MCP risk, AI inventory, and evidence screens.

Runtime Model

Show what the runtime decided and why.

Track what was allowed, warned, or blocked and why.

Decisioning Runtime outcomes
18% intervened
  • Allow82%
  • Warn10%
  • Block8%
Risk Sources Signals evaluated
Prompt
38%
MCP/tool
31%
Browser
19%
Output
12%

Control Surface

Built for the moments where AI becomes operational risk.

01

Runtime decisioning

Score prompt, browser, MCP, and output risk before the model/tool path proceeds.

02

MCP and tool control

Evaluate server trust, dangerous tools, sensitive arguments, and bulk export intent.

03

Evidence by default

Attach policy hits, reasons, tenant scope, and risk breakdowns to every decision.

Deployment Architecture

Put enforcement in the AI request path.

AIGuard sits between apps, agents, tools, and evidence stores as the policy decision layer.

App / AgentPrompt + context
AIGuardEvaluate policy
MCP / ToolsAuthorize action
EvidenceRecord decision
PolicyTenant-specific thresholds

Risk scoring and enforcement decisions remain scoped to the customer workflow.

ControlsBlock, warn, allow

Clear runtime outcomes for risky prompts, tools, browser activity, and outputs.

AuditDecision records

Retain reason, risk source, request context, policy result, and timestamp.

RolloutGateway pilot path

Start with one app or agent before expanding into broader AI inventory.

Security Posture

Control where AI data and tool actions go.

Gateway deployment, metadata-first evidence, tenant policy, explicit decisions.

Gateway-first

Place enforcement where prompts, tools, and outputs are executed.

Tenant policy

Thresholds, blocked servers, data handling rules, and exception paths.

Audit trail

Decision, reason, score, policy hit, and evidence record.

Integration ready

Prompts, browser signals, MCP, outputs, inventory, and audit stores.

Related Product

Turn runtime evidence into owner-ready security work.

Spectra uses agent findings and review states to move security work through triage, approvals, and release evidence.

Explore Spectra Watch Spectra demo
Spectra dashboard showing cross-agent observations, severity distribution, and triage rollups.

2-Week Evaluation

Pilot AIGuard on one AI workflow.

Bring one app or agent, one risky tool path, and a policy decision you need to prove before production rollout.

Start AIGuard pilot Watch demo
Week 1 Instrument the runtime path

Map prompt, browser, MCP/tool, output, and evidence touchpoints.

Week 2 Prove policy decisions

Validate block, warn, and allow behavior against real app workflows.

Success criteria
  • Risky AI action blocked before execution
  • External MCP access evaluated by tenant policy
  • Audit evidence generated for every decision