Shadow IT Discovery Orchestrator
Coordinates detection and governance of unapproved tools across the organization
Monitor network, identify unapproved tools, assess risk, notify owners, suggest alternatives
Shadow IT—unapproved cloud tools and SaaS applications—poses significant security and compliance risks while creating redundant costs. This agentic workflow continuously monitors network traffic to detect unapproved tools, identifies which users and departments are using them, assesses security and compliance risk for each discovery, notifies department heads with recommended approved alternatives, tracks adoption of sanctioned tools, and updates governance policies based on findings. By bringing shadow IT into the light without heavy-handed enforcement, this approach balances employee productivity needs with organizational security requirements. Enterprises implementing shadow IT discovery achieve 80% reduction in shadow IT risk while consolidating to approved tools and reducing software waste, with typical ROI of 6-10x through combined risk mitigation and cost savings. This is particularly critical for regulated industries—including financial services, healthcare, legal services, government agencies, and insurance—where unauthorized data processing or storage in unapproved cloud services can result in compliance violations, data breaches, and significant regulatory penalties.
Agent Architecture
A complex orchestrator managing network monitoring, risk assessment, notification, and governance policy updates to reduce shadow IT risk.
Shadow IT Discovery Orchestrator
Coordinates detection and governance of unapproved tools across the organization
Network Monitor
Monitors network traffic for unapproved SaaS tools
Risk Assessor
Assesses security and compliance risk
Notification Agent
Notifies department heads and recommends alternatives
Policy Updater
Updates governance policies based on findings
Monitor network traffic for unapproved SaaS tools
Identify users and departments using shadow IT
Assess security and compliance risk
Notify department heads and tool owners
Recommend approved alternatives
Track adoption of approved tools
Update governance policies
Click any KPI to view detailed measurement guidance, formulas, and typical ranges.
Capture all agent interactions (prompts, outputs, data sources accessed) in a central, searchable system
Central inventory of all agents with metadata: owner, purpose, data sources, risk level, users
Programmatically block prohibited actions (e.g., uploading PII to external models, accessing restricted data)
Ability to instantly disable any agent in case of security incident, data leak, or policy violation
Regularly test agents for vulnerabilities (jailbreaks, prompt injection, data exfiltration attempts)
These controls help ensure secure, compliant, and auditable AI operations. High-priority controls are critical for production deployment.
AI generating false or fabricated information presented as fact
AI using outdated data that no longer reflects current reality
Inability to verify or cite the original sources of AI-generated information
Users accessing data or performing actions beyond their permission level
Malicious manipulation of AI behavior through crafted input prompts
Unintentional exposure of sensitive data through model training or outputs
These risks should be mitigated through proper governance controls and operational procedures.
Validate identity, check policy, and grant or deny access requests automatically
Monitor AI systems for bias, compliance, data privacy, and regulatory adherence with automated audit trails
Orchestrate multi-environment deployments, validate compatibility, coordinate rollbacks, and manage release risks
Explore assistive AI tools that IT teams use to augment these agentic workflows.
Deploying AI agents in IT? Olakai gives you real-time monitoring, cost tracking, and governance across every agent in your stack.
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