aiFObserve
Distributed, Contextual Visibility & Governance for Enterprise AI Traffic. No Agents, No APIs, No
aiFObserve
Distributed, Contextual Visibility & Governance for Enterprise AI Traffic. No Agents, No APIs, No
aiFObserve – AI Observability & Governance from network telemetry
aiFObserve combines your network collected AI traffic and firewall logs with it’s own AI models & Apps intelligence to provide full Enterprise AI Governance through AI Usage, Cost Analysis, ROI, Projections, Recommendations, Security & Compliance violations and 11 regulation compliance evidence.
Any AI Observability product shows you what you spent on AI. We show you how to spend less, prove compliance, and see what's actually happening on your network - all in one platform, simply from your already collected data.
AI usage is moving across chatbots, co-pilots, developer tools, apps, agents, and direct model APIs. Much of that activity is invisible to traditional monitoring because it appears as encrypted outbound traffic, distributed sessions, or unmanaged provider access.
aiFObserve uses network collected flow records or firewall logs to identify AI models, AI Apps, users using them, quantify model-provider usage, detect shadow AI and other security risk patterns, and correlate traffic behavior with cost, security, and business context.
aiFObserve is built for enterprises that need to understand where AI traffic is going, who is using it, how much it costs, and whether usage creates business or security risk. It analyzes Netflows, IPFIX Biflows or Firewall & WAF derived network sessions and statistics to create a unified picture of AI activity across providers, internal users, applications, security risks and time.
The platform combines it’s built-in AI intelligence for AI usage discovery, behavioral analytics, cost modeling, security detection, rule-based governance, and recommendation generation so teams can move from raw flow telemetry to prioritized action.
Eight capabilities that make aiFObserve a practical observability layer for enterprise AI traffic.
Requires no Agents, no API Keys, no software to be installed or modifications to be made at your business end.
Implementation Note: Mirror the reference feature-card grid
Identifies known and unknown AI provider & App traffic from network collected data, including internal AI users, endpoints, destinations, traffic volume, and session patterns.
Implementation Note: Mirror the reference feature-card grid.
Builds normal traffic, user, application, and provider patterns so abnormal AI usage is easier to spot.
Implementation Note: Use concise card copy.
Turns flow volume, session duration, provider usage, tokens, prompt volume, and response activity into AI cost & usage intelligence.
Implementation Note: Use same card sizing as reference.
Flags data-exfiltration volume, unusual destinations, excessive sessions, cost amplification, slow responses, and shadow AI activity.
Implementation Note:Use detector language.
Lets teams express governance rules in plain language and convert them into detector-ready policies.
Implementation Note:Position as admin-friendly.
Combines usage, cost, trend, and projection outputs into recommendations for optimization and governance.
Implementation Note: Final card.
Creates evidence package for GDPR, SOC-2, HIPAA, EU AI-Act, CCPA and other regulations
Quantitative proof points for the aiFObserve IPFIX observability core. Replace target values with validated production benchmarks before launch if needed.
Effective identification of known AI provider traffic in validated environments.
Accurate surfacing of provider, user, and application-level cost outliers.
Observability without inline disruption to application traffic.
aiFObserve connects to your existing flow-export and analytics architecture. Deploy it alongside IPFIX collectors, SIEM pipelines, data lakes, or aiF session-stat sources to convert network telemetry into AI observability.
Ingest flow exports and logs from routers, firewalls, WAFs, cloud gateways, and network sensors/collectors.
Analyze provider usage, internal sources, time trends, cost patterns, and incident signals.
Publish executive summaries, security findings, cost optimization reports, and rule-driven alerts.
GET /aifobserv/status
200 OK - IPFIX Analytics Engine Online
// Distributed Engine Handshake
POST /aifobserve/analyze
{
"source_type": "ipfix",
"analysis_mode": "ai_traffic_observability",
"include_cost_model": true,
"include_security_detections": true,
}
aiFObserve for IPFIX is available for teams that want to turn existing network-flow data into AI usage, cost, risk, and ROI intelligence. Request a working session to review your telemetry sources, deployment architecture, and first reports.
IPFIX, session-stat, and flow-log onboarding Sample provider discovery and usage report Cost, security, and ROI analysis walkthrough24x5 Email Support Included