Vendor research
ModelOp
Review what this vendor says publicly, the security topics those statements may support, what remains unverified, and factual company context. This is not an assessment of product effectiveness or fit.
Use-case context
How this vendor relates to the selected use case
Related research availableBack to use case →
These links show approaches associated with this vendor. The relationship label describes how the approach maps to the use case—not product effectiveness, complete requirement coverage, or fit.
Company scale
Emerging?
EmergingAn early-stage provider with less than $25M in known funding, or 50 or fewer employees without at least $50M in known funding.This is a company-scale signal, not a product-quality rating.
?
EmergingAn early-stage provider with less than $25M in known funding, or 50 or fewer employees without at least $50M in known funding.This is a company-scale signal, not a product-quality rating.A descriptive band derived from retained public revenue, workforce, ownership, or funding signals.
Company scale is separate from product features, effectiveness, and suitability.
- $20M known funding
- Founded 2018
- Employee scale not sourced
- Private-company revenue and profitability not sourced
Company context
Private independent company
11-50 employees in the reviewed company profile, with named enterprise users including Fidelity Investments, FINRA, and Bristol Myers Squibb
Research coverageCounts describe available public research, not product quality.View details
- Vendor statements
- 17 records
- Source-checked records
- 5
- Evaluation requirements
- 17 in this research model
- Unresolved requirements
- 13
Company intelligence
Who is behind the product
Company facts provide evaluation context. Each signal is kept separate because tenure, workforce, funding, and hiring answer different questions.
FoundedModelOp states it was founded in 2018; predecessor company Open Data Group dates to 2016
HeadquartersChicago, Illinois
OwnershipPrivate independent company
EmployeesNot yet sourced
Capital and scaleIndependent company
ModelOp
- Known funding
- $20M
- Operating scale
- 11-50 employees in the reviewed company profile, with named enterprise users including Fidelity Investments, FINRA, and Bristol Myers Squibb
- Backing context
- $20M in known funding in the reviewed company snapshot; latest round was a $10M Series B led by Baird Capital in August 2024
Series B · $10M · 2024-08-19
Named investors
Baird Capital
Founders and leadership1 person listed
- Pete FoleyFormer
Co-Founder & Board Member; former CEO
Operating signalsRead each signal separately
There is no combined company rating. The company-scale label uses stated size thresholds; product features and effectiveness require separate evidence.
- Company tenure
- ModelOp states it was founded in 2018; predecessor company Open Data Group dates to 2016
- Workforce scale
- Not yet sourced
- Hiring activity
- Not displayed
A current count requires a retained, clickable source URL.
Open research questions
- Current employee range is not yet supported by a public source.
Company sources and research limits5 linked public sources
Only company facts supported by retained public sources are shown. Missing values remain unknown, and company scale does not establish product effectiveness.
Company sourceModelOp CenterModelOp describes end-to-end lifecycle governance for internal and vendor AI with policy, controls, testing, monitoring, cost, and value tracking.Company sourceModelOp company pageModelOp states that it was founded in 2018 and provides company and leadership context for its enterprise AI governance business.Company sourceModelOp Series B announcementModelOp announced a $10M Series B led by Baird Capital in August 2024 and described a blue-chip enterprise customer base.Company sourceModelOp Engage announcementModelOp identifies Fidelity Investments, FINRA, and Bristol Myers Squibb in its enterprise accelerator announcement.Company information sourceModelOp leadership transitionSupports the company facts shown in this profile.
Solution areas
These areas describe how the vendor approaches enterprise AI security. They do not establish product quality or fit.
Buyer context
- Treat ModelOp as an enterprise AI system-of-record and lifecycle control-plane candidate rather than a workforce data loss prevention (DLP) or browser enforcement product.
- Named blue-chip users support an enterprise-adoption signal, but $20M in known funding and undisclosed private-company revenue and profitability keep ModelOp in enhanced early-stage diligence.
- Public evidence covers internal and vendor AI, ML, generative AI, agents, risk tiering, approvals, testing, evidence, monitoring, cost, and value tracking.
Related frameworks
Where public vendor statements relate to framework requirements
11 related frameworks · expand when needed
Related frameworks
Where public vendor statements relate to framework requirements
These links show related requirements for further review. They do not establish framework compliance or control implementation. Open the full framework crosswalk →
Current referenceCSA AI Controls Matrix
- Requirements with public support
- 5
- Related requirements
- 14
- References
- 71
- Requirements with public support
- 5
- Related requirements
- 12
- References
- 35
- Requirements with public support
- 5
- Related requirements
- 12
- References
- 71
- Requirements with public support
- 5
- Related requirements
- 13
- References
- 39
- Requirements with public support
- 5
- Related requirements
- 12
- References
- 39
- Requirements with public support
- 5
- Related requirements
- 12
- References
- 31
- Requirements with public support
- 5
- Related requirements
- 13
- References
- 31
- Requirements with public support
- 5
- Related requirements
- 13
- References
- 25
- Requirements with public support
- 4
- Related requirements
- 11
- References
- 58
- Requirements with public support
- 4
- Related requirements
- 11
- References
- 27
- Requirements with public support
- 3
- Related requirements
- 3
- References
- 3
Evaluation questions
What to verify beyond public claims
These questions come from security requirements with some public support. Use them as starting points for demonstrations, documentation review, customer references, or a buyer-observed pilot.
- 01Approved AI usage monitoring
Approved AI workspace activity appears with user, workspace or tenant, model or provider, and timestamp.
- 02Approved AI usage monitoring
Prompt, model, or admin activity can be exported or correlated for the selected approved AI platform.
- 03AI governance, risk, and compliance
A test AI system is registered with owner, intended use, risk tier, lifecycle state, and applicable obligations.
- 04AI governance, risk, and compliance
A policy, assessment, approval, exception, or remediation workflow changes the governed state of the test system.
- 05AI assurance and adversarial testing
A controlled test campaign exercises an AI model, application, or agent against named AI attack classes.
- 06AI assurance and adversarial testing
Results include reproducible prompts or attack steps, affected component, severity, and remediation guidance.
- 07Action-taking agent monitoring
A test agent run captures plan, steps, tool calls, outcome, and timestamps.
- 08Action-taking agent monitoring
Agent memory, delegated task, autonomy, or runtime decision detail is visible in a timeline or log.
Detailed security-requirement research18 evaluation items · supporting evidence and open research are shown separatelyExpand
Security requirementPublic supportRelated frameworksWhat to verify
Unapproved AI use discovery
No supporting claim foundDiscover and monitor workforce AI tools, accounts, prompts, domains, models, users, and usage outside approved controls.
NIST AI RMF PlaybookNIST Cybersecurity Framework 2.0CIS Critical Security ControlsISO/IEC 42001OWASP GenAI Security Solutions Landscape
An unmanaged AI app used by a test user appears in discovery inventory with user, app or domain, and timestamp.
AI-feature discovery in business applications
No supporting claim foundInventory software as a service (SaaS) applications that embed AI features, expose enterprise data to AI capabilities, or create AI-driven data movement.
NIST AI RMF PlaybookNIST Cybersecurity Framework 2.0CIS Critical Security ControlsISO/IEC 42001OWASP GenAI Security Solutions Landscape
A software as a service (SaaS) app with an embedded AI feature appears in the software as a service (SaaS) AI inventory with app, provider, and feature context.
Approved AI usage monitoring
Strong public supportMonitor approved AI workspaces, tenants, gateways, and model platforms such as ChatGPT Enterprise, Claude Enterprise, Gemini, Microsoft Copilot, Vertex AI, Elvex, or internal AI gateways.
NIST AI RMF PlaybookNIST Cybersecurity Framework 2.0CIS Critical Security ControlsISO/IEC 42001OWASP GenAI Security Solutions Landscape
Approved AI workspace activity appears with user, workspace or tenant, model or provider, and timestamp.
Controls for unapproved AI use
No supporting claim foundBlock, coach, redirect, or contain non-approved AI use and policy-violating AI interactions.
NIST AI RMF PlaybookNIST Cybersecurity Framework 2.0CIS Critical Security ControlsISO/IEC 42001OWASP GenAI Security Solutions Landscape
A policy blocks, coaches, redirects, or contains a test interaction with an unapproved AI destination.
Sensitive-data protection for generative AI
No supporting claim foundDetect, classify, redact, or block sensitive data in prompts, responses, files, retrieval, memory, and AI-connected workflows.
NIST AI RMF PlaybookNIST Cybersecurity Framework 2.0CIS Critical Security ControlsISO/IEC 42001OWASP GenAI Security Solutions Landscape
Sensitive prompt, response, or file test data is detected and classified during an AI interaction.
Browser and business-application controls
No supporting claim foundApply session-level controls in browser and software as a service (SaaS) workflows, including uploads, downloads, copy/paste, sharing, and identity-aware access decisions.
NIST AI RMF PlaybookNIST Cybersecurity Framework 2.0CIS Critical Security ControlsISO/IEC 42001OWASP GenAI Security Solutions Landscape
A session-level policy controls upload, download, copy, paste, sharing, or form submission in a browser or software as a service (SaaS) workflow.
Generative AI application security
No supporting claim foundProtect enterprise-built large language model (LLM) applications, retrieval-augmented generation (RAG) systems, prompts, application programming interfaces (APIs), model calls, tools, and production runtime behavior.
NIST AI RMF PlaybookNIST Cybersecurity Framework 2.0CIS Critical Security ControlsISO/IEC 42001OWASP GenAI Security Solutions Landscape
A test large language model (LLM) application event records prompt, application programming interface (API), model, retrieval, or tool interaction context.
AI governance, risk, and compliance
Strong public supportInventory AI systems and owners, translate policy and regulatory obligations into governed workflows, assess risk, manage approvals and exceptions, and retain audit evidence across the AI lifecycle.
NIST AI RMF PlaybookNIST Cybersecurity Framework 2.0CIS Critical Security ControlsISO/IEC 42001OWASP GenAI Security Solutions Landscape
A test AI system is registered with owner, intended use, risk tier, lifecycle state, and applicable obligations.
AI assurance and adversarial testing
Limited public supportTest models, applications, retrieval-augmented generation (RAG) systems, and agents before release and continuously thereafter using adversarial probes, evaluation suites, attack simulation, and security release gates.
NIST AI RMF PlaybookNIST Cybersecurity Framework 2.0CIS Critical Security ControlsISO/IEC 42001OWASP GenAI Security Solutions Landscape
A controlled test campaign exercises an AI model, application, or agent against named AI attack classes.
AI model and supply-chain security
No supporting claim foundDiscover, inventory, scan, validate, and monitor models, datasets, model artifacts, registries, dependencies, and AI development assets for tampering, unsafe serialization, provenance gaps, or malicious content.
NIST AI RMF PlaybookNIST Cybersecurity Framework 2.0CIS Critical Security ControlsISO/IEC 42001OWASP GenAI Security Solutions Landscape
A test model or AI artifact appears in inventory with origin, version, hash or provenance, and deployment context.
AI gateway, tool-connection, and runtime controls
No supporting claim foundMediate model, agent, tool, application programming interface (API), connector, and Model Context Protocol (MCP) traffic through an enforcement point that applies identity-aware policy, content controls, routing, rate limits, and auditable allow or deny decisions.
NIST AI RMF PlaybookNIST Cybersecurity Framework 2.0CIS Critical Security ControlsISO/IEC 42001OWASP GenAI Security Solutions Landscape
A model, agent, tool, or Model Context Protocol (MCP) request passes through a named policy enforcement point.
Action-taking agent monitoring
Limited public supportObserve and govern agent plans, memory, tool calls, delegated tasks, autonomy, runtime decisions, and outcomes.
NIST AI RMF PlaybookNIST Cybersecurity Framework 2.0CIS Critical Security ControlsISO/IEC 42001OWASP GenAI Security Solutions Landscape
A test agent run captures plan, steps, tool calls, outcome, and timestamps.
Agent-to-agent communication security
No supporting claim foundAuthorize, log, and control agent-to-agent, agent-to-tool, Model Context Protocol (MCP), connector, and tool-chain handoffs.
NIST AI RMF PlaybookNIST Cybersecurity Framework 2.0CIS Critical Security ControlsISO/IEC 42001OWASP GenAI Security Solutions Landscape
An agent, tool, connector, or Model Context Protocol (MCP) handoff logs source identity, destination, and authorization decision.
Non-human identity and service-account security
No supporting claim foundInventory, least privilege, credential hygiene, monitoring, and lifecycle management for non-human identities, workloads, service accounts, application programming interface (API) keys, and machine credentials.
NIST AI RMF PlaybookNIST Cybersecurity Framework 2.0CIS Critical Security ControlsISO/IEC 42001OWASP GenAI Security Solutions Landscape
A test service account, agent identity, or non-human identity appears in inventory with owner and privileges.
AI agent identity and permissions
No supporting claim foundRegister AI agents as accountable identities, bind them to owners and delegating users, authorize task- and tool-level access, issue short-lived credentials, review access, and revoke or suspend agent authority.
NIST AI RMF PlaybookNIST Cybersecurity Framework 2.0CIS Critical Security ControlsISO/IEC 42001OWASP GenAI Security Solutions Landscape
A test agent is registered with a unique identity, accountable owner, purpose, and permitted resources.
AI coding-agent and workstation security
No supporting claim foundDiscover and govern AI coding agents, integrated development environment (IDE) assistants, command-line agents, skills, hooks, extensions, Model Context Protocol (MCP) tools, filesystem access, commands, network activity, secrets, and software-supply-chain actions on developer workstations and build environments.
NIST AI RMF PlaybookNIST Cybersecurity Framework 2.0CIS Critical Security ControlsISO/IEC 42001OWASP GenAI Security Solutions Landscape
A test coding agent and its skills, hooks, extensions, or Model Context Protocol (MCP) tools appear in an attributable inventory.
AI cost and usage controls
Limited public supportVisibility, attribution, budgeting, rate limiting, anomaly detection, and optimization for AI usage and spend across models, agents, workflows, and owners.
Commercial MetadataNIST AI RMF PlaybookNIST Cybersecurity Framework 2.0ISO/IEC 42001OWASP Agentic AI Security Solutions Landscape
A controlled AI usage event is attributed to user, team, model, workflow, or owner with cost or token metrics.
Licensing model
Research incompletePublicly discoverable commercial model such as per user, per seat, per app, per token, per integration, or enterprise platform license.
The vendor can map the sourced commercial model to per-user, per-seat, per-app, per-token, per-integration, or platform packaging.
Public sources
Vendor statements and quoted evidence
Showing the first 6 of 17 source records. Open additional records only when needed.
ModelOp materials reviewed did not provide a public claim for enterprise-wide discovery of unapproved AI use.
No quoted source text is recorded for this claim.
ModelOp materials reviewed did not provide a public claim for embedded AI discovery across the business-application environment.
No quoted source text is recorded for this claim.
ModelOp claims visibility into internal and vendor AI from a single enterprise system of record.
visibility into all internal and vendor AI
ModelOp materials reviewed did not provide a public claim for policy enforcement over unapproved AI use.
No quoted source text is recorded for this claim.
ModelOp materials reviewed did not provide a public claim for inspection or enforcement over sensitive data moving through AI.
No quoted source text is recorded for this claim.
ModelOp materials reviewed did not provide a public claim for browser or software as a service (SaaS)-session controls for employee AI use.
No quoted source text is recorded for this claim.
Show 11 additional evidence records
ModelOp materials reviewed did not provide a public claim for security controls for custom generative AI applications at runtime.
No quoted source text is recorded for this claim.
ModelOp claims end-to-end lifecycle governance with use-case intake, risk tiering, controls, approvals, evidence, monitoring, and attestations.
ModelOp automates end-to-end AI lifecycle management and governance
ModelOp claims automated testing for bias, drift, performance, documentation, and continuous risk evidence.
Continuously track risks and collect evidence to stay audit-ready
ModelOp materials reviewed did not provide a public claim for model and AI component supply-chain inspection.
No quoted source text is recorded for this claim.
ModelOp materials reviewed did not provide a public claim for inline model, agent, tool, application programming interface (API), or Model Context Protocol (MCP) policy enforcement.
No quoted source text is recorded for this claim.
ModelOp claims a system of record spanning machine learning, generative AI, and agents.
Establish visibility into all AI—ML, GenAI, Agents
ModelOp materials reviewed did not provide a public claim for trust or policy enforcement between agents.
No quoted source text is recorded for this claim.
ModelOp materials reviewed did not provide a public claim for non-human identity, service-account, secret, or workload credential lifecycle controls.
No quoted source text is recorded for this claim.
ModelOp materials reviewed did not provide a public claim for agent registration, delegated authorization, task-scoped access, and revocation.
No quoted source text is recorded for this claim.
ModelOp materials reviewed did not provide a public claim for coding-agent, integrated development environment (IDE), CLI, workstation, skill, hook, or package-action governance.
No quoted source text is recorded for this claim.
ModelOp claims portfolio-level dashboards and reporting for AI cost, value, return on investment (ROI), and use-case cost tracking.
Executive dashboards tracking AI value, cost, and risk over time