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Mindgard Limited Unclaimed

Cybersecurity

mindgard.ai

Mindgard is a leading AI security company helping enterprises discover, assess, and defend their AI systems with attacker-aligned, research-led defense across the AI lifecycle.

Mindgard Limited is an AI security company formed from more than a decade of AI security research at Lancaster University. It focuses on securing AI systems across the lifecycle by providing visibility into AI risk, assessments, and defenses against AI-specific threats. The organization serves enterprises that deploy AI, combining offensive security expertise with ongoing AI research to identify exploitable vulnerabilities in AI models and agents before attackers can exploit them. Mindgard emphasizes attacker-aligned security and responsible disclosure, offering capabilities such as AI threat discovery, adversarial testing, real-time threat detection, and governance and compliance, supported by education and professional services. It aims to advance a secure AI ecosystem through a research-led, collaborative approach.

Secure the World's AI.

What we offer

Mindgard Platform

Comprehensive AI security solution ensuring robust protection against evolving threats.

mindgard.ai/ai-security-platform

AI Security Training

Service

Enhance your team's expertise in AI security through practical training on fundamental concepts and adversarial techniques.

mindgard.ai/services/ai-security-training

Technical Account Management

Service

Enhance your Mindgard experience with expert guidance and operational support.

mindgard.ai/services/technical-account-manager

Market segments

Market size by segment

Growth potential (CAGR)

AI governance and model risk management

5.7 Billion USD12.9% CAGR

Capabilities to inventory AI systems, maintain an AI risk register, assess model risks, and enforce governance controls for responsible AI and regulatory expectations.

Adversarial testing and threat modeling

1.05 Billion USD13.84% CAGR

Offensive testing, red-team assessments, and threat modeling applied to applications and AI/ML systems to identify vulnerabilities, attack vectors, and remediation guidance.

Runtime protection and monitoring

2.5 Billion USD25.1% CAGR

Real-time detection of threats and anomalous behavior in production environments, including runtime anomaly monitoring and automated response to active attacks.

AI asset discovery and attack surface management

0.3 Billion USD30% CAGR

Discovery and reconnaissance of shadow AI and distributed AI assets to map the AI attack surface and prioritize exposure reduction.

AI security training and workforce development

0.45 Billion USD24% CAGR

Instructor-led education and hands-on workshops to build offensive and defensive AI security skills, operational readiness, and secure development practices.

More information about our offering

Mindgard Platform

Mindgard Platform is an attacker-aligned, enterprise-grade AI security platform that maps AI risk across discovery, reconnaissance, red-teaming, assessment, runtime protection, model scanning, governance and compliance, and education services to identify, assess, and defend AI systems across their lifecycle.

  • Identify Security Weaknesses
    This feature enables users to uncover vulnerabilities in AI systems, facilitating timely remediation and enhanced security.
  • Simulate Real-World Attacks
    Regularly challenges AI systems with adversarial testing to ensure resilience against new attack strategies.
  • Real-Time Defense
    Protects AI models by continuously monitoring and responding to threats as they occur.
  • Proactive Threat Identification
    Utilizes offensive security techniques to preemptively identify and mitigate potential risks.
  • Reveal Vulnerabilities
    Simulates detailed attacker behavior to discover hard-to-find vulnerabilities in AI systems.
  • Identify Areas of Exposure
    Thorough assessments help prioritize improvements and align security strategies with best practices.
  • Map AI Risks
    Provides visibility into AI assets and potential attack vectors, helping to mitigate hidden risks.
  • Scan for Vulnerabilities
    Assesses AI models to find exploitable vulnerabilities and policy violations.
  • Automate Security Checks
    Ensures continuous security validation with every model or code change, reducing potential exposure.
  • Build Practical Skills
    Interactive workshops empower teams with essential skills to recognize and counteract AI-specific security threats.
  • Ensure Compliance
    Helps organizations maintain compliance with evolving AI regulations and standards.
  • Understand Threat Landscape
    Facilitates deeper understanding of risks and their relevance to organizational security through effective analysis.
  • Guarantee Testing Integrity
    Validates that all tests are executed correctly and findings are easily actionable.
  • Align Security Efforts
    Continuous support ensures that your security measures remain effective and up-to-date against evolving threats.
  • Enhance Security Knowledge
    Mindgard provides comprehensive training and resources to develop essential knowledge in AI security practices.
  • Demonstrate Effectiveness
    Provides documentation of real-world applications and effectiveness of Mindgard solutions in securing AI infrastructures.
  • Streamline Integration
    Facilitates a smooth onboarding process to ensure your team can start testing efficiently.

AI Security Training

Mindgard’s AI Security Training delivers instructor-led sessions that establish a shared understanding of AI security concepts, adversarial techniques, and secure AI practices, with hands-on practice to improve defenses.

  • Test And Strengthen Defenses
    Equip your team with the knowledge to identify and mitigate adversarial attacks effectively.
  • Build Security Foundations
    Provide a comprehensive understanding of essential AI security principles, enabling informed decision-making.
  • Implement Secure Development
    Learn best practices for securing AI systems, from design through deployment and operation.
  • Practice Real-World Security
    Engage in practical exercises to build hands-on skills in securing AI systems against common threats.
  • Enhance Operational Readiness
    Prepare your security teams to effectively defend against evolving AI threats.

Technical Account Management

Mindgard’s Technical Account Management provides a dedicated expert to guide onboarding, deployment, ongoing support, and operational assurance to maximize value from the Mindgard platform and services.

  • Guarantees Reliable Testing
    Provides continual support to maintain high-fidelity testing processes and address issues promptly.
  • Facilitates Efficient Integration
    Ensures smooth onboarding processes that align the Mindgard system with existing enterprise operations.
  • Aligns With Security Objectives
    Offers tailored recommendations and quarterly reviews to enhance defensive strategies.
  • Enhances Workflow Efficiency
    Aids teams in maximizing the effectiveness of tools through automated processes and integration.

References

Methodology and sourcing behind the figures shown above.

AI governance and model risk management

Estimate based on multiple specialized market reports for AI model risk management: three independent vendors report a 2024–2025 market base around USD 5.7–5.87 billion and CAGRs in the ~12.5–12.9% range. I used the MarketsandMarkets 2024 model-risk-management estimate (USD 5.7B; 12.9% CAGR) corroborated by Polaris and SNS Insider figures showing similar base sizes and ~12.5–12.8% growth forecasts.

Adversarial testing and threat modeling

Estimate anchored to recent market reports in the search results. Credence Research values the threat-modeling tools market at USD 1,049.6M (2024) with a 13.84% CAGR; MarketsandMarkets gives a similar multi-year trajectory (USD 0.8B in 2022 to USD 1.6B by 2027, ~14.9% CAGR). One outlier (MarketResearchFuture) reports a much larger scope ($12.55B in 2024, CAGR 16.57%)—likely a broader definition. I adopt the near-term consensus (~USD 1.05B) and ~14% CAGR as the best-supported estimate for this segment.

Runtime protection and monitoring

Multiple independent market reports in the provided search results place the runtime/runtime-application self-protection market around USD 1.1–3.1 billion (2023–2025). Midpoint consensus (~USD 2.5B) is used as the current market-size estimate. Reported forecast CAGRs for the niche range from ~20.8% to 32.1%; averaging those independent forecasts and weighting to recent 2024–2026 baselines yields an estimated growth potential of ~25.1% CAGR. For context, the broader application-security market is larger but grows more slowly (~11.5% CAGR).

AI asset discovery and attack surface management

Search results show the broader attack-surface/CAASM markets range from roughly $0.9B–$2.8B (2024–2025) with CAGRs from ~17%–29%. No source isolates AI-asset discovery specifically, so I treated it as a nascent subset of ASM/CAASM. Using a mid-range ASM base (sources below) and assuming AI-asset discovery/shadow-AI mapping represents ~10%–12% of current ASM/CAASM spend (given early adoption but high priority), I estimate a current market size ≈ $0.3B. Because AI asset discovery demand should outpace general ASM (rapid AI adoption, shadow-AI risk), I project an elevated CAGR (~30%), above most ASM averages.

AI security training and workforce development

Estimated global market for instructor-led AI security training and hands-on workforce development is derived as a subset of the broader AI in corporate training market (USD 2.57B in 2025, CAGR ~22.8%) and U.S. AI training-related markets (U.S. AI training datasets market USD 627.8M in 2023, CAGR 24.8%). Given strong reported demand and rising cybersecurity training budgets for AI-specific skills, a focused AI security training segment is estimated at roughly 15–20% of corporate AI training in the near term, yielding ~USD 0.45B and a growth potential around 24% CAGR.

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