The missing policy layer for agentic AI — Skyrelis offers real-time security and policy enforcement for production environments without requiring code changes.
Comprehensive inventory of agents, tools, and data access patterns.
Control agent's tools/actions & guardrails by tenant/region, in real time with no app changes.
ML-driven and mission-aware risk score to prioritize threats and reduce false positives.
Enabling high-velocity internal AI automation while maintaining security visibility, behavioral insight, and governance — without becoming a bottleneck.
Your organization is deploying AI agents to accelerate internal work — Sales Ops updating your CRM, Finance processing expenses, HR answering policy questions. The gains are real, but so is the new attack surface.
What tools, services and datasets are your agents accessing?
Agents chain calls across databases, APIs, and third-party services, creating data flows that slip past traditional perimeter security.
Is this normal or suspicious?
A sudden spike in database queries could be legitimate automation — or an internal data exfiltration attempt.
How do I enforce policy without blocking productivity?
Lock down the agent too tightly, and employees route around it with shadow AI. Leave it open, and a misconfigured agent might leak sensitive payroll data.
Skyrelis provides behavioral visibility and intelligent enforcement for internal AI agents. Instead of static rules, we use agent profiling, tool cataloging, and ML-driven risk scoring to understand what agents are doing — and whether it's aligned with their mission.
Automatically discovers and catalogs every agent — their role, autonomy level, connected tools, and data sensitivity. Each receives a baseline risk score.
Every tool, API, database, and external service is mapped into a unified catalog and scored for risk based on data sensitivity, action severity, and integration exposure.
Enforces policies based on context — agent role, user group, data sensitivity, and real-time risk score. Graduated enforcement (monitor → warn → block) improves over time.
| Capability | Value | Business Impact |
|---|---|---|
| Agent Visibility & Ownership | See every agent, what it can access, and who owns it. Reduce shadow AI and blind spots. | Fewer surprises. Faster approval to scale AI safely. |
| Risk Scoring & Prioritization | Rank agents and actions by risk (data sensitivity + action severity + behavior signals). | Less security overhead. Avoid hiring just to keep up. |
| Central Policy Layer | Define and enforce policies in one place without rebuilding apps. | Lower engineering cost. Roll out controls quickly. |
| Sensitive Data Controls | Detects and blocks sensitive data leaving approved boundaries. | Reduce breach and regulatory exposure. Protect IP/PII. |
| Policy Staging | Start in monitor mode, tune with real activity, then enforce. | Fewer workflow breaks. Less shadow AI bypass. |
| Audit Evidence | Log who triggered what, what the agent did, and what data/tools were involved. | Faster audits. Lower total cost of risk. |
An enterprise deploys an AI agent to help sales teams summarize deal notes, update pipeline stages, and generate forecast reports. The CRM contains sensitive data — contract values, competitive intelligence, and executive-level deal terms.
Skyrelis profiles the agent, catalogs its CRM tool access, and establishes behavioral baselines. The ML engine monitors for anomalies. When a junior rep uses the agent, Skyrelis dynamically redacts executive-level information based on the user's role.
The Sales team gets productivity benefits while the CISO maintains visibility, behavioral insight, and policy enforcement — without writing custom security code.
Delivering customer-specific security policies for AI agents embedded in your SaaS product — without code changes or deployment delays.
You've built a powerful AI agent into your product — a travel booking assistant, customer support copilot, or financial advisory tool. Your customers love it. But now your enterprise prospects are asking hard questions:
"Can we restrict what data the agent can access for our users?"
"We need HIPAA-compliant behavior for healthcare clients — can you guarantee that?"
"Our EU customers require GDPR controls. How do you handle regional differences?"
The result: deployment paralysis. You can't scale your AI-powered product without scaling your security engineering burden.
Skyrelis decouples security policy from product code. Our platform sits in the runtime path of your AI agent, applying customer-specific policies dynamically — without touching your application.
Automatically catalogs your agent's mission, autonomy level, tool access, and data sensitivity — establishing a baseline risk profile.
Every tool, API, and data source your agent accesses are mapped and scored for risk, enabling intelligent policy decisions.
Define differentiated security profiles per customer tier, industry vertical, or regulatory zone. Same agent, same codebase.
Policies are applied at runtime, intercepting prompts, tool calls, and outputs automatically based on customer segment.
| Capability | Value | Business Impact |
|---|---|---|
| Zero-Code Policy Management | Turn enterprise requirements into industry and geo-specific controls without separate builds. | Lower engineering cost. Faster releases. |
| Customer-Specific Controls | Apply per-customer requirements (HIPAA, SOC 2, GDPR) from one product, with no forks. | Win more enterprise deals. Reduce maintenance cost. |
| Sales Acceleration | Show clear controls early in the sales cycle and avoid months of custom security work. | Shorter sales cycles. Higher enterprise conversion. |
| Audit-Ready Evidence | Provide audit-ready evidence of policy enforcement per interaction (who/what/when/which control). | Reduce audit cost. Lower compliance-driven churn. |
A travel booking startup has an AI concierge agent that helps users plan and book trips. They've landed a major corporate client, but the deal requires restricting access to personal travel history, enforcing expense policy limits, and logging all booking decisions for audit.
Using Skyrelis, the product team creates a "Corporate Enterprise" policy profile. When users from that customer interact with the agent, Skyrelis automatically enforces data access restrictions, validates bookings against expense thresholds, and generates compliance logs — all without a single line of code change.
The startup closes the enterprise deal in weeks instead of months, while their engineering team stays focused on product innovation — not compliance engineering.
We operate in the agent execution path, not as a monitoring afterthought. Policies are enforced in real time, before actions complete.
No code changes required. Deploy policies centrally and apply them across your agent fleet without developer sprints.
Add or update guardrails as policies, without reworking app code. Compose controls over time and roll them out centrally.
Behavioral baselines and anomaly detection that understand agent intent, not just network traffic patterns.
Start in monitor mode, tune with real activity data, then enforce high-confidence policies. Reduce friction, eliminate shadow AI.
Get started with Skyrelis and bring governance to your agentic AI — without slowing down innovation.