Inferensys

Service

AI Copilot and Assistant Usage Fencing

Inference Systems engineers technical guardrails and data loss prevention (DLP) policies for AI copilots like GitHub Copilot and Microsoft 365 Copilot to prevent the submission of proprietary code, PII, or regulated data to external models, securing your enterprise's most valuable assets.
Security engineer implementing LLM guardrails on laptop, safety rules visible on screen, technical implementation session.
SHADOW AI DETECTION

Your Developers Are Using AI Copilots. Is Your Data Secure?

Implement technical guardrails to prevent proprietary code and regulated data from leaking to external AI models.

Unmanaged AI copilot usage is the single largest vector for intellectual property and PII leakage. We build the fences.

Our AI Copilot and Assistant Usage Fencing service implements Data Loss Prevention (DLP) policies as code for tools like GitHub Copilot and Microsoft 365 Copilot. We prevent the submission of:

  • Proprietary source code to external training data
  • Personally Identifiable Information (PII) and Protected Health Information (PHI)
  • Regulated financial data and internal communications

We deploy network-level monitoring and endpoint agents to detect and block unauthorized API calls, providing a real-time inventory of all AI tool usage across your enterprise. This foundational visibility is the first step in our comprehensive Shadow AI Detection and Security Posture Management offering.

Deliverables & Outcomes:

  • Zero data exfiltration via sanctioned or unsanctioned AI assistants
  • Automated policy enforcement integrated directly into developer IDEs and SaaS applications
  • Detailed audit trails for compliance with GDPR, HIPAA, and SOC 2
  • Prioritized risk dashboard showing exposure by team, application, and data type

Without these guardrails, your organization faces unquantified compliance risk and the irreversible loss of competitive advantage. For a complete assessment of your AI security posture, explore our Shadow AI Risk Assessment and Quantification service.

DELIVERABLES

Business Outcomes: Secure Productivity, Maintained Compliance

Our AI Copilot fencing service provides the technical guardrails that allow developers to use AI assistants safely, eliminating the trade-off between innovation speed and data security. We deliver measurable outcomes that protect your most valuable assets.

01

Proprietary Code Protection

Implement granular DLP policies to prevent the submission of proprietary source code, algorithms, and internal APIs to external AI models like GitHub Copilot. We enforce policy-as-code within your IDE and CI/CD pipelines.

100%
Policy Enforcement
Zero Leakage
Guarantee
02

PII & Regulated Data Fencing

Deploy real-time content scanning and redaction for AI prompts, automatically masking Personally Identifiable Information (PII), PHI, and financial data before it leaves your secure environment, ensuring compliance with GDPR, HIPAA, and CCPA.

< 50ms
Scanning Latency
HIPAA/GDPR
Compliance Ready
03

Context-Aware Usage Policies

Move beyond simple blocklists. We engineer dynamic policies that understand code context, allowing safe use of public libraries while blocking sensitive business logic. Policies adapt based on project, user role, and data classification.

Contextual
Policy Engine
Role-Based
Access Control
04

Centralized Audit & Forensics

Gain complete visibility with detailed logs of all AI assistant interactions. Our integration provides forensic-ready audit trails for compliance reporting and rapid incident response, feeding directly into your SIEM and SOAR platforms.

Immutable Logs
For Audits
SIEM/SOAR
Integration
05

Developer Experience Optimization

Security that doesn't slow down development. We design fencing rules that are transparent to developers for safe tasks and provide clear, actionable feedback when a policy is triggered, maintaining productivity without friction.

No Friction
For Safe Use
Clear Guidance
On Blocked Actions
06

Continuous Policy Refinement

Our service includes ongoing monitoring and tuning of your fencing policies. We analyze usage patterns and false positives to continuously refine rules, balancing security rigor with operational efficiency as your AI usage evolves.

Proactive Tuning
Service
Reduced False Positives
Outcome
From Discovery to Policy Enforcement

Typical Implementation Timeline: 4-6 Weeks

Our structured engagement delivers a fully operational AI Copilot fencing system, including discovery, policy design, integration, and deployment. This timeline is based on a standard enterprise environment with 1-2 primary data sources (e.g., GitHub, Microsoft 365).

Phase & Key DeliverablesWeeks 1-2Weeks 3-4Weeks 5-6

Discovery & Inventory

Complete

• Network scanning for unsanctioned AI usage

• API call log analysis (GitHub, Microsoft 365)

• Risk assessment report & policy scoping

Policy Design & Guardrail Development

In Progress

Complete

• Custom DLP rule creation for proprietary code/PII

• Granular policy definition (allow/block/redact)

• Integration with existing IAM & data classification

Integration & Deployment

In Progress

Complete

• Deployment of monitoring agents & API gateways

• Integration with SIEM/SOAR for alerting

• End-user communication & training materials

Validation & Handoff

Complete

• Policy effectiveness testing & validation

• Production deployment & performance baseline

• Documentation & ongoing support plan

Ongoing Support & Management

Optional SLA

Optional SLA

Optional SLA

24/7 Monitoring & Alert Tuning

Available

Quarterly Policy Reviews & Updates

Available

Integration with new AI tools & models

Available

ENTERPRISE FOCUS

Industries We Secure

Our AI Copilot and Assistant Usage Fencing service is engineered for sectors where data sovereignty, intellectual property protection, and regulatory compliance are non-negotiable. We implement technical guardrails that prevent sensitive data from leaving your environment.

Technical Implementation & Security

AI Copilot Fencing: Frequently Asked Questions

Get specific answers on how we implement technical guardrails for AI copilots like GitHub Copilot and Microsoft 365 Copilot to prevent data leakage.

Standard deployments for policy configuration and DLP integration take 2-4 weeks. Complex environments with legacy systems or custom ERPs may require 4-6 weeks. We follow a phased approach: 1-week discovery and risk assessment, 1-2 weeks for policy-as-code development, and 1-2 weeks for integration and testing. For a foundational view of your AI landscape, consider our Enterprise Shadow AI Discovery and Inventory Service.

Prasad Kumkar

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.