See, control, and prove every AI decision across your
Security leaders are accountable for AI risk across the whole company, yet most have little visibility into what teams actually do with LLMs. Igris gives you a live risk heat map, enforced policy at the proxy layer, and a complete audit trail, so shadow AI, incident response, and board reporting all run from one control plane.
Real Time AI Risk Visibility
The board asks about your AI security posture. You have no concrete answer, just a rough sense of which teams are using which tools, collected through informal check ins and fragmented spreadsheets. You know AI adoption is accelerating. You do not know where the actual exposure lives. Without a real time view of AI security posture across your entire organization, every board conversation about AI risk is built on estimates.
Igris Observe gives you a live AI risk heat map that scores every AI system in your environment across four categories, with a green, amber, or red status for each one. Open the dashboard and see your company's entire AI security posture at a glance: which systems are compliant, which are flagged, and what changed since the last board meeting. No manual aggregation. No stale data pulled from five different sources the night before.
You walk into the boardroom with a real number, a real trend, and a real answer. AI risk posture stops being a conversation about what you believe, and becomes a conversation about what the data shows.
Incident Response and Clustering
An LLM agent starts sending customer data to an external provider. By the time anyone notices, through a user complaint, a downstream data mismatch, or a chance audit, the exposure is already hours or days old. You have no way to reconstruct the exact chain of events, no clear answer on how much data was exposed, and no certainty the leak has actually stopped. Without structured AI incident response capabilities, your team is always reacting too late.
Igris automatically groups related audit events into a single incident. Dozens of PII redaction events from the same connection within a few minutes are clustered together, assigned a severity level from critical down to informational, and surfaced in one view. Drill into any incident and see exactly which user or agent was involved, which connection and model were used, what was redacted, and the precise timestamp for every event in the chain. Webhook alerts fire the moment an incident is created, notifying your Slack or Discord channel so the engineer on call can respond immediately.
Your team responds in minutes. You have a complete AI incident record, who, what, which model, which connection, how much, ready for compliance review or postmortem the moment the incident closes. No reconstruction work. No gaps in the timeline.
Weekly and Monthly Board Reports
Every reporting cycle, someone on your team spends hours pulling data from different systems, reformatting numbers, and assembling a deck that looks like it was put together the night before, because it was. The output never looks as authoritative as it should, and the data is already a few days old by the time leadership sees it. AI governance reporting should not consume the hours of your most senior security people.
Igris generates polished, branded PDF reports automatically. Each report includes an executive summary, the live AI risk heat map, compliance status across every AI system, and trend data showing whether AI security posture is improving or getting worse since the last period. Configure the date range, attach your team name and logo, and schedule delivery through the API. The finished report arrives in leadership's inbox on schedule, weekly or monthly, without anyone lifting a finger.
AI governance reporting shifts from a manual sprint to a background process. Leadership gets a consistent, professionally formatted view of AI security posture every cycle. Your team gets the hours back, and the reports stop looking like something assembled under pressure.
Policy Enforcement Across All AI Traffic
Security decides that no customer facing system should use a specific model, or that every prompt must be scanned for PII before it leaves the network. You document the policy. You send it to engineering teams. Weeks later, you discover it is being followed inconsistently, some teams missed the memo, others interpreted it differently. You have no way to verify compliance short of reviewing individual codebases one by one. LLM policy enforcement cannot rely on individual team discipline.
Igris turns a security decision into an enforced control at the proxy layer. Create a model deny rule. Attach a content guard with detectors for SSNs, credit card numbers, email addresses, and 20+ other sensitive data types. Igris applies these rules to every LLM call across every connection, without requiring development teams to touch a line of code. Every time a rule fires, you receive a webhook alert and a permanent entry in the audit trail.
LLM policy enforcement is no longer a document you hope teams read. It is a control enforced on every call, with a verifiable record of every enforcement decision aligned to NIST AI RMF governance requirements. Compliance is no longer assumed, it is provable.
Shadow AI Detection
Shadow AI is now the most common entry point for data leakage in enterprise environments. Teams quietly open their own API accounts, experiment through personal keys, or connect to models through tools your security function never approved. None of that traffic flows through your monitoring. None of it is logged. Research shows 76% of organizations have confirmed shadow AI incidents, and most security teams discover them only after data has already left the organization's control boundary. Under the EU AI Act and GDPR, ungoverned AI usage creates regulatory exposure that shadow AI makes impossible to close: you cannot demonstrate compliance for systems you cannot see.
Once teams are onboarded to Igris, all LLM traffic routes through one proxy, giving security complete visibility into every connection. The dashboard shows which connections are active, which sit idle, and how much traffic each one generates. Anomaly detection flags unusual patterns: a new connection suddenly processing ten thousand requests in a day surfaces immediately. A deny by default policy means no new connection can move any traffic until it is explicitly reviewed and approved.
Shadow AI stops being an unmanaged blind spot and becomes a visible, governable surface. New AI usage gets caught at the approval gate, not discovered months later during a breach investigation or EU AI Act audit.
Third Party AI Vendor Risk Assessment
You are evaluating several LLM providers simultaneously. Legal and security both want assurance that no sensitive data reaches any of them during the trial. There is no clean way to enforce that today, you are relying on engineering teams to self police what goes into evaluation prompts. Third party AI vendor risk assessment cannot rest on trust alone, and it cannot be verified after the fact.
Set up a separate connection for each provider in Igris. Apply identical PII redaction policies across all of them. Route all evaluation traffic through the proxy. Igris logs every call across every provider, flags every policy violation, and produces comparative audit data showing which provider triggered the most enforcement events. Cost and latency are visible side by side in the same interface, no separate spreadsheet required.
Vendor selection is driven by actual usage data, policy violation rates, latency, cost, compliance behavior, not by trust or self reported assurances. You have documented proof that no sensitive data reached any provider during the evaluation, which satisfies internal security review and third party AI vendor risk assessment requirements simultaneously.
Bring your AI risk posture under control
Book a walkthrough of the Igris risk heat map, shadow AI detection, and policy enforcement built for security and CISO teams.