AI gateway and tool-connection controlsAdditional if: Model or tool traffic uses a security gateway · Calls to models, tools, or connections are routed through a security gateway, including Model Context Protocol (MCP) connections.Action-taking agent safeguardsAdditional if: AI takes multi-step actions · The employee-facing AI performs multiple steps or takes actions rather than only generating content.
Vendor research
Mindgard
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.
- $8M known funding
- 11-50 employees
- Founded 2022
Company context
Private independent company; reviewed Mindgard-controlled sources do not identify an acquirer or parent company
Mindgard says it secured Fortune 500 design partners in 2026
Research coverageCounts describe available public research, not product quality.View details
- Vendor statements
- 19 records
- Source-checked records
- 12
- Evaluation requirements
- 19 in this research model
- Unresolved requirements
- 7
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.
Founded2022
HeadquartersBoston and London
OwnershipPrivate independent company; reviewed Mindgard-controlled sources do not identify an acquirer or parent company
Employees11-50
Capital and scaleIndependent company
Mindgard
- Known funding
- $8M
- Operating scale
- Mindgard says it secured Fortune 500 design partners in 2026
- Backing context
- Mindgard says its 2024 seed round was led by security investors; investor names were not stated on the reviewed company page
Funding round · $8M · 2025-01-16
Named investors
.406 Ventures · Atlantic Bridge · Willowtree Investments · IQ Capital · Lakestar
Founders and leadership1 person listed
- Dr. Peter GarraghanCurrent role listed
Founder & Chief Science Officer
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
- 2022
- Workforce scale
- 11-50
- Hiring activity
- Not displayed
A current count requires a retained, clickable source URL.
Open research questions
- A current hiring source is not available, so the count is not shown.
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 sourceMindgard offensive AI security pageMindgard describes continuous attacker-aligned testing across models, agents, applications, development, deployment, and runtime.Company sourceMindgard about pageMindgard states that it was founded in 2022, spun out of Lancaster University research, and is headquartered in Boston and London.Company information sourceStored company websiteSupports the company facts shown in this profile.Company information sourceMindgard funding announcementSupports the company facts shown in this profile.Company information sourceMindgard LinkedIn company profileSupports 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.
AI testing and adversarial assuranceCore product focusAI application runtime protectionCore product focusAI asset and configuration securityRelated coverageAI governance, risk, and complianceRelated coverageAI gateway and tool-connection controlsRelated coverageAction-taking agent safeguardsRelated coverage
Buyer context
- Treat Mindgard as an offensive AI security and assurance platform with adjacent discovery, governance, model scanning, and runtime protection capabilities.
- Public evidence supports AI and shadow-AI inventory, agent and tool-call visibility, continuous red teaming, multimodal testing, model scanning, framework mapping, CI/CD integration, and inline runtime enforcement.
- Public pages reviewed did not establish browser-session controls, workforce unapproved-AI enforcement, generic non-human identity (NHI) lifecycle, agent identity issuance, coding-agent workstation controls, or AI spend governance.
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
- 11
- Related requirements
- 14
- References
- 71
- Requirements with public support
- 10
- Related requirements
- 11
- References
- 58
- Requirements with public support
- 10
- Related requirements
- 12
- References
- 35
- Requirements with public support
- 10
- Related requirements
- 12
- References
- 71
- Requirements with public support
- 10
- Related requirements
- 13
- References
- 39
- Requirements with public support
- 10
- Related requirements
- 12
- References
- 39
- Requirements with public support
- 10
- Related requirements
- 12
- References
- 31
- Requirements with public support
- 10
- Related requirements
- 11
- References
- 27
- Requirements with public support
- 10
- Related requirements
- 13
- References
- 31
- Requirements with public support
- 10
- Related requirements
- 13
- References
- 25
- 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.
- 01Unapproved AI use discovery
An unmanaged AI app used by a test user appears in discovery inventory with user, app or domain, and timestamp.
- 02Unapproved AI use discovery
The test user's AI usage activity can be filtered or exported with AI-specific context.
- 03AI-feature discovery in business applications
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.
- 04AI-feature discovery in business applications
The inventory shows which users, data classes, integrations, or providers are associated with the AI-enabled software as a service (SaaS) app.
- 05Approved AI usage monitoring
Approved AI workspace activity appears with user, workspace or tenant, model or provider, and timestamp.
- 06Approved AI usage monitoring
Prompt, model, or admin activity can be exported or correlated for the selected approved AI platform.
- 07Sensitive-data protection for generative AI
Sensitive prompt, response, or file test data is detected and classified during an AI interaction.
- 08Sensitive-data protection for generative AI
A policy redacts, blocks, coaches, or records the sensitive data event before it leaves the approved path.
Detailed security-requirement research18 evaluation items · supporting evidence and open research are shown separatelyExpand
Security requirementPublic supportRelated frameworksWhat to verify
Unapproved AI use discovery
Strong public supportDiscover 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
Limited public supportInventory 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
Strong public supportDetect, 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
Strong public supportProtect 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
Strong 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
Limited public supportDiscover, 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
Limited public supportMediate 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
Strong 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
No supporting claim foundVisibility, 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
Limited public supportPublicly 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 19 source records. Open additional records only when needed.
Mindgard claims AI inventory risk reports that identify shadow AI and map AI infrastructure components and tool calls.
map every component of your AI infrastructure, enumerate every tool call, and identify shadow AI
Mindgard claims continuous testing for third-party AI applications as well as internally built systems.
ensuring the security of both third-party AI applications and in-house systems
Mindgard claims discovery and inventory of AI applications, models, and agents across the enterprise environment.
Discover and inventory AI applications, models, agents, and shadow AI across your entire environment.
Mindgard materials reviewed did not provide a public claim for workforce allow, coach, restrict, or block policy over unapproved AI use.
No quoted source text is recorded for this claim.
Mindgard claims inline detection and enforcement for data leakage in AI application traffic.
inline detection and enforcement for prompt injection, data leakage, and tool abuse
Mindgard materials reviewed did not provide a public claim for browser session controls over upload, download, copy, paste, sharing, or form submission.
No quoted source text is recorded for this claim.
Show 13 additional evidence records
Mindgard claims inline runtime detection and enforcement for prompt injection, data leakage, and tool abuse.
inline detection and enforcement for prompt injection, data leakage, and tool abuse
Mindgard claims audit-ready reports and finding mappings to OWASP, MITRE, NIST, and AIUC-1.
Generate evidence, remediation guidance, and audit-ready reports mapped to OWASP, MITRE, NIST, and AIUC-1.
Mindgard claims continuous attacker-aligned security testing and automated red teaming across development, deployment, and runtime.
continuously pressure-test AI systems the same way real attackers do, across development, deployment, and runtime
Mindgard claims runtime model scanning for security vulnerabilities, safety risks, exploitable behaviors, and harmful outputs.
scans AI models for both security vulnerabilities and safety risks
Mindgard claims inline runtime enforcement with block, alert, and enrich actions for prompt injection, data leakage, and tool abuse.
configurable block/alert/enrich options
Mindgard claims inventory reports that enumerate AI tool calls and map connected AI infrastructure.
map every component of your AI infrastructure, enumerate every tool call
Mindgard materials reviewed did not provide a public claim for authenticating, authorizing, or enforcing policy over agent-to-agent communication.
No quoted source text is recorded for this claim.
Mindgard materials reviewed did not provide a public claim for workload identities, service accounts, application programming interface (API)-key discovery, secret rotation, or machine-credential lifecycle management.
No quoted source text is recorded for this claim.
Mindgard materials reviewed did not provide a public claim for agent registration, delegated authorization, short-lived credential issuance, or agent identity lifecycle controls.
No quoted source text is recorded for this claim.
Mindgard materials reviewed did not provide a public claim for enforcing coding-agent commands, filesystem or network actions, skills, hooks, extensions, packages, or workstation activity.
No quoted source text is recorded for this claim.
Mindgard materials reviewed did not provide a public claim for AI spend attribution, budgets, chargeback, rate limits, or token-cost anomaly detection.
No quoted source text is recorded for this claim.
Mindgard states that commercial pricing is tailored through its sales team.
Contact sales for tailored pricing.
Mindgard claims integration across CI/CD, integrated development environment (IDE), SIEM, ticketing, and security testing workflows.
Integrates seamlessly across developer and security workflows, including CI/CD pipelines, IDE hooks, SIEM, and ticketing systems.