Enterprise security teams face a challenge that traditional asset inventory tools weren’t designed to solve. AI tools now run across browsers, SaaS platforms, developer environments, and production workflows. The old approach of cataloging servers and endpoints doesn’t capture which models employees use, what data flows through prompts, or where shadow AI creates exposure. AI discovery and inventory software addresses this gap by mapping real AI activity to users, teams, and business units with investigation-ready context.
This comparison examines three approaches to AI visibility: ModelSight, Zenity, and HiddenLayer. Each platform takes a different angle on discovery, governance, and workflow integration. By the end, you’ll know which tool aligns with your security operations and governance requirements.
Key Takeaways: ModelSight vs Zenity vs HiddenLayer
- ModelSight delivers complete AI inventory across sanctioned, third-party, and shadow AI in hours, with posture scoring inside a week.
- Traditional asset tools miss AI-specific interaction context such as prompts, model identification, and user attribution.
- Zenity focuses on AI agent security and governance with runtime boundaries and agentic identity management.
- HiddenLayer offers model scanning, red teaming, and AI supply chain security for organizations building or deploying ML systems.
- ModelSight connects AI usage discovery with contextual risk understanding and actionable detection for security workflows.
ModelSight vs Zenity vs HiddenLayer: Overview
What is ModelSight?
ModelSight is an AI security visibility platform that surfaces AI usage across enterprise environments. The platform discovers every model, endpoint, and integration in use, then maps that activity to users, teams, and business units. Security teams gain investigation-ready evidence without deploying agents or rewriting their existing security programs.
ModelSight key features
- Complete AI inventory: Every endpoint, owner, model, and call volume across sanctioned, third-party, and shadow AI usage.
- Posture scoring: Score each surface against your governance baseline to identify ungoverned usage, exposed credentials, and policy drift.
- Response integration: Route events to your existing SIEM, SOAR, PagerDuty, and Slack channels with full context.
- Agentless deployment: Observe existing egress, proxy, and VPC flow logs without code changes or host agents.
- Investigation support: Event details with request and response bodies, filtering by user or time window, and audit logs.
ModelSight pros and cons
Pros:
- Deploys in hours with read-only connectors and no agent installation required.
- Captures 99.4% of inference calls without inline overhead on any host.
- Combines discovery breadth with contextual investigation detail for cross-functional review.
Cons:
- Focused on visibility and investigation rather than inline blocking, which suits teams prioritizing understanding before enforcement.
- Dashboard interface may require initial orientation for analysts unfamiliar with AI-specific telemetry.
- Endpoint monitoring requires the ModelSight agent when proxy or CASB coverage isn’t available.
What is Zenity?
Zenity offers an AI agent security and governance platform designed to secure autonomous agent decisions. The platform surfaces exposure, steers actions, and closes gaps across SaaS, custom, and endpoint deployments. Zenity builds live inventories of agents and evaluates how they’re configured against policy.
Zenity key features
- AI observability: Builds a live inventory of agents across SaaS, custom, and endpoint deployments while tracking data each agent touches.
- AI security posture management: Evaluates agent configuration and permissions against policy before deployment.
- Runtime boundaries: Evaluates every agent action in real time to allow, block, or shut down based on policy.
- Agentic identity and access management: Correlates identity from Okta and Microsoft Entra with actual agent behavior.
- AI detection and response: Detects runtime threats and blocks unsafe actions mapped to OWASP and MITRE ATLAS.
Zenity pros and cons
Pros:
- Coverage spans agentic SaaS (Salesforce Agentforce, Copilot Studio), cloud, and homegrown agents.
- Offers runtime boundaries that can evaluate and block agent actions in real time.
- Includes guardian agents that triage and investigate security events automatically.
Cons:
- Primary focus is on AI agents rather than broader AI tool usage across employees, contractors, and technical teams.
- Does not address visibility into browser-based AI tools, personal AI accounts, or non-agentic AI interactions.
- Runtime enforcement requires policy definition before observing actual usage patterns.
What is HiddenLayer?
HiddenLayer offers an AI security platform for discovering models, securing workflows, and preventing AI attacks. The platform addresses AI supply chain security, runtime protection, and attack simulation across enterprise AI deployments. HiddenLayer targets organizations building, deploying, or operating ML systems in production.
HiddenLayer key features
- AI discovery: Gains visibility into AI assets across environments to eliminate shadow AI through automated inventory.
- AI supply chain security: Validates model integrity and supply chain before deployment to catch vulnerabilities early.
- AI runtime security: Detects and responds to AI attacks without impacting performance in production environments.
- AI attack simulation: Simulates real-world AI attacks to uncover weaknesses before adversaries do.
- AI guardrails: Enforces safe, compliant, and policy-aligned AI behavior across applications and business units.
HiddenLayer pros and cons
Pros:
- Covers the full AI lifecycle from development through production deployment with scanning and testing.
- Model-agnostic and agentless deployment with no training data exposure required.
- Integrates with existing CI/CD, MLOps, SIEM, and SOAR tooling for workflow fit.
Cons:
- Primary focus is on model security and ML systems rather than employee AI usage patterns across the organization.
- Does not address interaction-level visibility into prompts, responses, and user behavior across SaaS and browser-based AI.
- Red teaming and attack simulation orientation suits teams with established AI security programs rather than those building initial visibility.
ModelSight vs Zenity vs HiddenLayer: In-depth comparison
AI usage discovery and inventory
ModelSight discovers AI usage across reverse proxy, CASB integrations (Cloudflare, Netskope, Zscaler), and Microsoft connectors without requiring self-reporting or manual declarations. The platform builds a complete inventory of sanctioned, third-party, and shadow AI in hours. Zenity and HiddenLayer focus discovery on agents and ML models respectively, which leaves gaps in browser-based and SaaS-embedded AI tool visibility.
For enterprise security teams, the difference matters operationally. An engineer pasting production queries into ChatGPT, a marketing analyst using an unsanctioned AI writing tool, or a contractor accessing an unapproved code assistant all create exposure that agent-focused or model-focused platforms don’t capture.
Interaction-level context and investigation
Investigation workflows require context beyond asset identification. ModelSight captures request bodies, response bodies, user attribution, and timestamps for every AI interaction. When security analysts need to investigate whether sensitive data reached an external model, they can filter events by user, time window, or business unit and export evidence for downstream review.
Zenity offers identity context for agent actions but focuses on runtime decisions rather than forensic investigation of past activity. HiddenLayer addresses model security and attack detection but does not capture the prompts and responses that flow through employee AI interactions. Traditional asset tools track device counts and software versions without this AI-specific telemetry.
Shadow AI detection
Shadow AI represents unsanctioned tool adoption that security teams cannot see with traditional inventory approaches. ModelSight surfaces shadow AI as employees adopt new services, tracking free trials, personal accounts, and unvetted platforms across the organization. The platform identifies 12+ shadow AI tools per organization on average based on industry research.
Zenity detects shadow AI within its agent security scope, particularly through OAuth grants and third-party integrations. HiddenLayer addresses shadow AI through its discovery module but emphasizes model inventory over user behavior patterns. For security leaders, the question is whether you need to see every AI tool employees touch or only the agents and models your engineering teams deploy.
Security workflow integration
Security operations run through existing tools. ModelSight sends events to your SIEM with proper context, routes alerts to PagerDuty and Slack, and supports SOAR integration for automated response. A SIEM tells you what already happened. ModelSight gives you visibility into the AI surface itself before incidents occur, then feeds events back with the attribution security teams need.
Zenity extends existing security workflows with AI-specific threat detection mapped to OWASP and MITRE ATLAS. HiddenLayer integrates with cloud providers, CI/CD pipelines, and security platforms for MLOps-centered teams. The integration question depends on whether your primary workflow gap is AI usage across employees or AI model security across your engineering pipeline.
Compliance and audit readiness
Auditors ask how organizations monitor AI tools that access customer data. ModelSight offers audit trails for AI usage, documentation of where regulated data flows, and detection of policy breaches before auditors find them. The platform supports SOC 2 control evidence (CC7.2, CC7.3, CC6.1) and helps teams prepare for EU AI Act and ISO 42001 audits with defensible records.
Zenity supports compliance workflows with governance controls and policy enforcement for AI agents. HiddenLayer addresses model governance and AI security posture for compliance-conscious deployments. For regulated industries such as healthcare, finance, and government, the coverage question is whether auditors will ask about employee AI tool usage or ML model security, or both.
Deployment and time to value
ModelSight deploys with read-only connectors to existing observability surfaces in about 30 minutes. First complete inventory arrives in roughly four hours. First posture score comes within a week after tuning against your governance program. No agents on hosts, no code changes, and no disruption to production environments.
Zenity deploys through API integrations and supports various SaaS, cloud, and endpoint environments. HiddenLayer offers agentless deployment across any architecture. All three platforms prioritize deployment speed, but the value metric differs: ModelSight delivers usage inventory, Zenity delivers agent governance, and HiddenLayer delivers model security posture.
Comparison table: The best AI security visibility for enterprise workflows
| Capability | ModelSight | Zenity | HiddenLayer |
|---|---|---|---|
| Shadow AI tool discovery | ✓ | Partial (agents only) | Partial (models only) |
| Prompt and response capture | ✓ | ✗ | ✗ |
| User attribution per interaction | ✓ | ✓ | ✗ |
| Time to first inventory | 4 hours | Days | Days |
| Browser-based AI visibility | ✓ | ✗ | ✗ |
| SIEM/SOAR integration | ✓ | ✓ | ✓ |
Why ModelSight is the best for enterprise security workflows
Enterprise security leaders need to answer fundamental questions before they can write policy, score risk, or respond to incidents. Which AI tools are employees using? What data flows through those tools? Who uses unsanctioned services? ModelSight delivers those answers in hours rather than weeks, with the investigation context that distinguishes routine activity from material exposure.
The platform’s visibility-first approach aligns with how security programs mature. You understand before you enforce. Traditional asset inventory tools track what software runs on endpoints. Agent-focused platforms govern autonomous systems. Model security platforms protect ML pipelines. ModelSight fills the gap between them by capturing how people actually interact with AI across your organization.
If your security team needs to detect risky AI behavior, investigate incidents with evidence, and support auditors with defensible records, ModelSight is the operational fit. Book a demo to see your AI inventory in 30 minutes with your actual data.
FAQs: AI Discovery and Inventory Software vs Traditional Asset Inventory Tools
What makes AI inventory software different from traditional asset management?
Traditional asset management tracks devices, software installations, and infrastructure components. AI inventory software discovers the models, endpoints, and interactions that happen across SaaS platforms, browsers, and APIs. ModelSight captures which users interact with which AI tools and what data flows through those interactions, giving security teams context that device inventories cannot offer.
Can traditional CASB and DLP tools handle AI visibility requirements?
CASB and DLP tools see app usage and file movements but miss AI-specific interaction context. They can tell you someone accessed ChatGPT but not what prompts they submitted or which models processed sensitive data. ModelSight extends your CASB investment by adding the AI-specific telemetry your existing tools lack.
How does ModelSight discover shadow AI across an organization?
ModelSight connects to your existing observability surfaces including reverse proxy, CASB, and VPC flow logs. The platform identifies AI API calls as they happen, maps them to users and business units, and flags unsanctioned services in real time. Security teams see shadow AI adoption before it entrenches, with the context needed to make governance decisions.
What investigation capabilities does AI inventory software offer?
ModelSight captures event details including request and response bodies, user attribution, timestamps, and model identification. Security analysts can filter events by user, time window, or incident scope and export evidence for legal, compliance, or HR review. This forensic record supports cross-functional investigations that traditional tools cannot.
How quickly can security teams deploy AI discovery software?
ModelSight deploys with read-only connectors in approximately 30 minutes. First complete AI inventory arrives within four hours. First governance posture score comes within a week. No agents, no code changes, and no disruption to production systems during deployment.
Does AI inventory software replace existing security tools?
No. ModelSight complements your existing SIEM, SOAR, CASB, and DLP investments. The platform sends AI events back into your SIEM with proper context and routes alerts to your existing response channels. Your security program doesn’t need a rewrite. It needs AI-specific visibility layered into what you already run.