Mindgard Limited Unclaimed
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-platformAI Security Training
Enhance your team's expertise in AI security through practical training on fundamental concepts and adversarial techniques.
mindgard.ai/services/ai-security-trainingTechnical Account Management
Enhance your Mindgard experience with expert guidance and operational support.
mindgard.ai/services/technical-account-managerMarket segments
Market size by segment
Growth potential (CAGR)
AI governance and model risk management
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
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
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
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
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 WeaknessesThis feature enables users to uncover vulnerabilities in AI systems, facilitating timely remediation and enhanced security.
- Simulate Real-World AttacksRegularly challenges AI systems with adversarial testing to ensure resilience against new attack strategies.
- Real-Time DefenseProtects AI models by continuously monitoring and responding to threats as they occur.
- Proactive Threat IdentificationUtilizes offensive security techniques to preemptively identify and mitigate potential risks.
- Reveal VulnerabilitiesSimulates detailed attacker behavior to discover hard-to-find vulnerabilities in AI systems.
- Identify Areas of ExposureThorough assessments help prioritize improvements and align security strategies with best practices.
- Map AI RisksProvides visibility into AI assets and potential attack vectors, helping to mitigate hidden risks.
- Scan for VulnerabilitiesAssesses AI models to find exploitable vulnerabilities and policy violations.
- Automate Security ChecksEnsures continuous security validation with every model or code change, reducing potential exposure.
- Build Practical SkillsInteractive workshops empower teams with essential skills to recognize and counteract AI-specific security threats.
- Ensure ComplianceHelps organizations maintain compliance with evolving AI regulations and standards.
- Understand Threat LandscapeFacilitates deeper understanding of risks and their relevance to organizational security through effective analysis.
- Guarantee Testing IntegrityValidates that all tests are executed correctly and findings are easily actionable.
- Align Security EffortsContinuous support ensures that your security measures remain effective and up-to-date against evolving threats.
- Enhance Security KnowledgeMindgard provides comprehensive training and resources to develop essential knowledge in AI security practices.
- Demonstrate EffectivenessProvides documentation of real-world applications and effectiveness of Mindgard solutions in securing AI infrastructures.
- Streamline IntegrationFacilitates 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 DefensesEquip your team with the knowledge to identify and mitigate adversarial attacks effectively.
- Build Security FoundationsProvide a comprehensive understanding of essential AI security principles, enabling informed decision-making.
- Implement Secure DevelopmentLearn best practices for securing AI systems, from design through deployment and operation.
- Practice Real-World SecurityEngage in practical exercises to build hands-on skills in securing AI systems against common threats.
- Enhance Operational ReadinessPrepare 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 TestingProvides continual support to maintain high-fidelity testing processes and address issues promptly.
- Facilitates Efficient IntegrationEnsures smooth onboarding processes that align the Mindgard system with existing enterprise operations.
- Aligns With Security ObjectivesOffers tailored recommendations and quarterly reviews to enhance defensive strategies.
- Enhances Workflow EfficiencyAids 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.
- Market size was valued at USD 1,049.6 million in 2024; expected to reach USD 2,960.62 million by 2032, CAGR 13.84%.
- grow from an estimated USD 0.8 billion in 2022 to USD 1.6 billion by 2027 (CAGR 14.9%).
- 2024 Market Size $12.55 Billion; CAGR 16.57% (2025-2035).
- $1.08 Bn in 2024, projects $4.29 Bn by 2034, representing a 14.8% CAGR.
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).
- The Runtime Application Self-Protection Market reached USD 2.16 billion in 2025 (CAGR 25.8%).
- RASP Market size was USD 2,482.10 million in 2024; CAGR 23.84% to 2032.
- Runtime Application Self-Protection Market size was valued at USD 3.08 billion in 2024; CAGR 20.80% (2025–2032).
- Global RASP market size recorded at USD 1,128.4 million in 2023; projected CAGR 32.05% (2024–2031).
- Application security market projected to reach USD 23.45 billion by 2031; CAGR 11.5% (2026–2031).
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.
- projected to expand from USD 0.9 billion in 2024 to USD 3.3 billion by 2029, CAGR of 29.3%
- global attack surface management market size was valued at USD 1.03 billion in 2025 ... to USD 5 billion by 2034, exhibiting a CAGR of 21.03%
- Global Cyber Asset Attack Surface Management market valued at $2.8 billion in 2025 ... CAGR of 17.1%
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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