1. Continuous AI Red Teaming Is Essential for Modern Security. S&P Global emphasizes that generative AI introduces constantly evolving risks—from prompt injection to data leakage—that traditional, periodic red teaming cannot keep up with. Continuous, automated testing of AI models and applications is now a requirement, not an option, to protect against emerging threats.
2. Mindgard Redefines Red Teaming with a Technology-First Approach. Mindgard leads with automation and AI-driven testing while keeping humans involved to interpret nuanced issues such as bias and trust. This combination delivers scalable, repeatable, and cost-efficient AI security testing that integrates directly into enterprise workflows.
3. Research Depth and Market Timing Give Mindgard an Edge. With its roots in academic research at Lancaster University and strong backing from leading investors, Mindgard is well positioned to meet the rapidly growing demand for AI security. As the “agentic economy” reshapes how software operates, Mindgard’s continuous AI red teaming provides the foundation for safer, faster AI innovation.
Generative AI is changing how organizations build, deploy, and defend software. But with new capabilities come new vulnerabilities. A recent S&P Global Market Intelligence 451 Research report highlights why continuous AI red teaming is essential to securing modern AI systems and how Mindgard is defining this new category of AI security.
The report, “Mindgard’s Continuous AI Red Teaming Looks to Secure Models and Applications,” examines how automation, research, and continuous testing intersect to protect organizations from AI-specific risks.
While the discipline of red teaming is not new, with security teams actively emulating adversarial tactics, techniques and procedures, Mindgard looks to primarily use a technology and automation-first approach with humans in the loop for additional reinforcement, as the verification of generative AI harms like large language model jailbreaking, bias or abuse requires human interpretation for now.
Red teaming has long been part of cybersecurity, where human experts emulate attackers to find weaknesses. But generative AI introduces novel threats such as prompt injection, data leakage, model extraction, and jailbreaks. These threats evolve continuously, so defenses must evolve too.
Mindgard’s approach leads with technology and automation to simulate adversarial behavior at scale while keeping a human in the loop to interpret issues like bias, trust, and safety. Mindgard is providing red teaming for generative AI rather than simply automating GenAI tools for conventional red teaming or penetration testing. This combination of automation and human expertise allows organizations to test continuously and keep pace with AI’s rapid development.
The S&P Global report notes that the broader AI ecosystem is in constant motion. Innovations such as Anthropic’s Model Context Protocol and Google’s Agent2Agent make it easier to connect and orchestrate AI systems, but they also create new security exposures. As the report explains, “the potential growth for attack surface area from poorly implemented integrations could be very high.” The takeaway: continuous red teaming is no longer optional.
The report places Mindgard within a broader transformation in software. Businesses are moving from Software-as-a-Service (SaaS) to Service-as-Software, where AI agents perform work autonomously rather than simply assisting humans.
This shift reshapes market economics, productivity, and security. As AI systems act independently, organizations must test and validate their behavior across complex, interconnected systems. Mindgard’s continuous red teaming provides a way to measure and mitigate those risks before they lead to exploitation.
Mindgard offers a productized service rather than traditional consulting. Customers connect their existing AI models and applications to the Mindgard platform, which runs automated attack simulations to uncover risks such as:s:
Each test produces detailed results mapped to OWASP and MITRE frameworks, along with recommended mitigations. Findings integrate into SIEM, ticketing, and reporting tools so security teams can act within their existing workflows.
Continuous testing of GenAI in models and applications will remain critical. Newer GenAI-based services may not need to cross the same competitive moats as previous generations of cloud and SaaS, so Mindgard’s addressable market surface area could grow geometrically. Although recent innovations, such as Anthropic’s Model Context Protocol and Google’s Agent2Agent, may streamline interfaces, standardize security between agents and accelerate integration between multiple models, the potential growth for attack surface area from poorly implemented integrations could be very high. The underlying GenAI red teaming needs to be continuous in part because the entire ecosystem is changing at all levels. Profound continuous changes in unit, user and technology provider economics; technology; the harms; and adversaries will require constant adjustment. Mindgard must continuously sharpen and adjust its focus to add immediate value and refine the iterative approach needed for success.
S&P Global highlights that Mindgard’s continuous testing model removes dependency on hourly consulting and manual cycles, making AI security scalable and repeatable.
Mindgard focuses on organizations that are building or adopting AI at scale, from technology providers and software vendors to large enterprises accelerating AI integration. Its platform reduces time to market while ensuring that innovation happens safely.
The report also notes opportunities for collaboration with bug bounty platforms and security research groups such as HackerOne and Synack. These communities could integrate Mindgard’s technology into their testing frameworks to expand coverage.
Mindgard’s academic foundation is another differentiator. The company was founded by Professor Peter Garraghan of Lancaster University, an EPSRC Fellow specializing in AI and ML security, and Steve Street, an experienced entrepreneur. Backed by investors including IQ Capital, .406 Ventures, Atlantic Bridge, Lakestar, Osney Capital, and WillowTree Investments, Mindgard blends cutting-edge research with practical application to stay ahead of emerging AI risks.
AI red teaming is becoming a crowded market. Companies like Prompt Security, Protect AI, Calypso AI, Robust Intelligence, and Lakera have entered the space, along with data security vendors such as Varonis and Proofpoint.
S&P Global observes that while the segment lacks clear boundaries, the opportunity is expanding quickly. With more than 1.5 million AI models available on Hugging Face, the need for scalable, continuous testing is immense. Mindgard’s automation-first platform and strong research partnerships position it to stand out amid this growth.
Critical to modern app development, incorporating the AI model layer requires understanding and mitigating the weaknesses and risks for underlying services and models. The combination of new risks and new opportunities for vendors and their enterprise customers will require additional approaches.
S&P Global concludes that as AI becomes more dynamic and interconnected, organizations will need continuous, intelligent, and research-backed testing to manage risk. Mindgard’s combination of automation, human insight, and academic rigor positions it as a leader in this emerging discipline.
“GenAI fire will be required to fight GenAI fire,” the report states. Mindgard is turning that insight into action, helping enterprises secure the next generation of intelligent systems.
