Your developers are using unsanctioned AI tools. Our API call monitoring service provides the critical visibility you lack, detecting calls to external AI providers like OpenAI and Anthropic before sensitive data leaves your network.
Service
API Call Monitoring for Unauthorized AI Integrations
Deploy network-level and endpoint monitoring to detect and alert on API calls to external AI providers, preventing sensitive data exfiltration through unvetted integrations.
SHADOW AI DETECTION
The Unseen Risk: Unauthorized AI Integrations Leaking Your Data
Deploy network-level monitoring to detect and block sensitive data exfiltration via unvetted AI APIs.
- Real-time Detection: Identify API calls from any endpoint, SaaS application, or custom integration.
- Immediate Alerts: Receive instant notifications for policy violations or suspicious data volumes.
- Contextual Intelligence: Correlate activity with user, department, and data sensitivity tags.
Prevent a data breach by gaining control over the AI tools your teams are already using.
This service is the first technical control in a complete AI Security Posture Management (AI-SPM) strategy. It provides the foundational data for our Shadow AI Risk Assessment and Quantification and integrates directly with your existing SIEM/SOAR platforms.
ACTIONABLE SECURITY
Tangible Outcomes of Enterprise AI API Monitoring
Our API call monitoring service delivers concrete, measurable improvements to your security posture and operational governance, moving beyond simple detection to active risk management.
01
Real-Time Threat Detection & Alerting
Deploy network-level sensors and endpoint agents that instantly detect and alert on API calls to unauthorized AI providers like OpenAI, Anthropic, or Midjourney, preventing sensitive data exfiltration before it occurs.
< 5 sec
Mean Time to Detect
Zero
False Positives Guarantee
02
Quantified Risk Exposure Reports
Receive executive-level dashboards that quantify data leakage risk, map shadow AI usage to specific departments, and calculate potential financial exposure from compliance violations, enabling data-driven remediation.
100%
Visibility Coverage
Prioritized
Remediation Roadmap
03
Automated Policy Enforcement
Automatically block high-risk API calls based on configurable data loss prevention (DLP) policies and user roles, integrating with tools like Microsoft Purview to enforce governance without manual intervention.
99.9%
Policy Accuracy
Automated
Incident Response
04
Compliance Audit Trail Generation
Generate immutable, detailed logs of all AI-related API activity to demonstrate compliance with GDPR, HIPAA, and internal governance frameworks, simplifying regulatory audits and internal reviews.
Fully Immutable
Audit Logs
GDPR/HIPAA
Compliance Ready
06
Cost Attribution & Showback
Attribute unsanctioned AI service consumption from cloud bills and API logs back to specific teams or projects, enabling accurate showback, chargeback, and optimization of sanctioned AI budgets.
100%
Cost Attribution
Optimized
AI Budgets
Build vs. Buy Comparison
Comprehensive API Monitoring Coverage Matrix
A detailed comparison of the time, cost, and risk involved in building an API call monitoring solution in-house versus partnering with Inference Systems for a managed service.
| Monitoring Capability | Build In-House | Inference Systems Managed Service |
|---|---|---|
Time to Deploy Full Coverage | 6-12 months | 4-8 weeks |
Initial Detection of Unauthorized AI Calls | Manual, post-hoc log review | Real-time, automated alerting |
Coverage for SaaS & Third-Party Apps | Limited (requires agent deployment) | Comprehensive (network-level + endpoint) |
Pre-built Integrations (OpenAI, Anthropic, etc.) | You develop and maintain | Included and continuously updated |
Security & Audit Risk | High (untested, unaudited code) | Low (audited, battle-tested platform) |
Ongoing Tuning & Threat Intelligence | Your security team's responsibility | Managed by our AI security experts |
Integration with SIEM/SOAR | Custom development project | Pre-built connectors included |
Uptime & Support SLA | Defined by your team | 99.9% with 24/7 dedicated support |
Total First-Year Cost (Engineering + Ops) | $250K - $600K+ | $80K - $200K |
Guaranteed Outcome | Uncertain coverage, delayed ROI | Reduced data exfiltration risk within 60 days |
ENTERPRISE PROTECTION
Critical Use Cases for API Call Monitoring
Our API call monitoring service delivers immediate visibility and control. It is engineered to detect and prevent unauthorized AI integrations before they lead to data exfiltration, compliance violations, or unexpected costs.
01
Prevent Sensitive Data Exfiltration
Deploy real-time monitoring agents to detect and block API calls containing PII, PHI, or intellectual property sent to external AI providers like OpenAI or Anthropic. This directly addresses the core data leakage risk of shadow AI.
Real-time
Detection & Blocking
Zero Trust
Data Sovereignty
02
Enforce AI Usage Policies
Automatically enforce granular, role-based policies on which AI services, models, and endpoints are permitted. Block unauthorized SaaS integrations and personal API key usage at the network level.
Policy-as-Code
Enforcement
Role-Based
Access Control
04
Integrate with SIEM/SOAR for Incident Response
Stream enriched API call alerts directly into your existing Security Information and Event Management (SIEM) and SOAR platforms. Unify AI security events with enterprise-wide incident response workflows.
Native
SIEM Integration
Automated
Alert Enrichment
05
Secure AI Copilot and Assistant Usage
Implement data loss prevention (DLP) fencing for tools like GitHub Copilot and Microsoft 365 Copilot. Prevent the submission of proprietary code, internal documents, and regulated data to external model endpoints.
Inline
Content Inspection
Pre-emptive
Data Protection
06
Demonstrate Regulatory Compliance
Generate immutable audit trails of all AI-related API traffic. Map data flows to specific regulatory articles (GDPR, HIPAA, EU AI Act) to prove data sovereignty and maintain compliance during audits.
Immutable
Audit Logs
Article Mapping
for GDPR/HIPAA
SHADOW AI DETECTION
API Call Monitoring for Unauthorized AI Integrations
Deploy network-level monitoring to detect and block sensitive data exfiltration via unvetted AI APIs.
Our four-phase process delivers a production-ready monitoring system within 4-6 weeks, providing immediate visibility into all AI API traffic across your network and endpoints.
Phase 1: Discovery & Baseline
- Conduct a comprehensive network scan to map all active AI service endpoints (
api.openai.com,api.anthropic.com, etc.). - Establish a traffic baseline to distinguish sanctioned from unsanctioned usage.
- Deliver a real-time inventory of all AI integrations within 10 business days.
Phase 2: Policy & Rule Engineering
- Collaborate with your security team to define allow/block/alert policies based on data classification, user groups, and applications.
- Engineer custom detection rules for SaaS applications and internal tools making covert API calls.
- Integrate with your existing SIEM/SOAR and Data Loss Prevention (DLP) systems for unified response.
Phase 3: Deployment & Instrumentation
- Deploy lightweight endpoint agents and network sensors with zero impact on application performance.
- Implement encrypted traffic analysis to maintain visibility without breaking TLS.
- Configure real-time alerts for policy violations sent directly to your SOC.
Phase 4: Operational Handoff & Reporting
- Provide a centralized dashboard for ongoing monitoring, showing API call volumes, risk scores, and user attribution.
- Deliver weekly compliance reports mapping AI data flows to GDPR Article 35 and HIPAA requirements.
- Conduct a knowledge transfer session with your IT and security teams for long-term management.
This service is part of our broader Shadow AI Detection and Security Posture Management pillar, which includes Enterprise Shadow AI Discovery and AI-SPM Integration.
Enabling Efficiency, Speed & Accuracy
Intelligent Analysis, Decision & Execution
We build AI systems for teams that need search across company data, workflow automation across tools, or AI features inside products and internal software.
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Give teams answers from docs, tickets, runbooks, and product data with sources and permissions.
Useful when people spend too long searching or get different answers from different systems.
Enterprise searchRAGPermissions
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Automate internal workflows
Use AI to route work, draft outputs, trigger actions, and keep approvals and logs in place.
Useful when repetitive work moves across multiple tools and teams.
AI agentsWorkflow automationGovernance
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Add AI to products and internal tools
Build assistants, guided actions, or decision support into the software your team or customers already use.
Useful when AI needs to be part of the product, not a separate tool.
AI integrationDecision supportModel routing
Read moreTechnical Implementation Details
API Call Monitoring: Frequently Asked Questions
Get specific answers on how we deploy network-level monitoring to detect and prevent unauthorized AI integrations that risk sensitive data exfiltration.
We deploy lightweight agents at your network egress points and on critical endpoints to inspect outbound traffic. Using a combination of signature-based detection (for known AI provider domains/IPs) and behavioral analysis (for anomalous data volumes to new endpoints), we identify calls to services like OpenAI, Anthropic, and other LLM APIs. Alerts are generated in real-time with full context: user, destination, data volume, and sensitivity tags based on your DLP policies. This provides the foundational visibility described in our Enterprise Shadow AI Discovery and Inventory Service.
About the author
Prasad Kumkar
CEO & MD, Inference Systems
Prasad Kumkar is the CEO & MD of Inference Systems and writes about AI systems architecture, LLM infrastructure, model serving, evaluation, and production deployment. Over 5+ years, he has worked across computer vision models, L5 autonomous vehicle systems, and LLM research, with a focus on taking complex AI ideas into real-world engineering systems.
His work and writing cover AI systems, large language models, AI agents, multimodal systems, autonomous systems, inference optimization, RAG, evaluation, and production AI engineering.
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How We Work
Custom AI workflows for your Business
One-fit-all AI don't work for modern businesses. At Inferensys, we aim to understand your business & custom requirements; which we use to define most efficient agentic workflows, the data, and the tools for your business.
01
Review the use case
We understand the task, the users, and where AI can actually help.
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Pick the right approach
We define what needs search, automation, or product integration.
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Build the first useful version
We implement the part that proves the value first.
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Improve from there
We add the checks and visibility needed to keep it useful.
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