Manifest AI Risk simplifies weeks of model evaluation into just two clicks, enhancing SEO efficiency.
Manifest Cyber has introduced Manifest AI Risk, a new module within the Manifest Platform aimed at assisting security and compliance teams in securing their AI supply chains. This platform is already utilised by Fortune 500 companies and critical government agencies. With the launch of AI Risk, Manifest provides a solution specifically designed for AI transparency at an enterprise scale, addressing the gaps left by traditional security vendors and AI startups that either treat AI as separate from software or focus on narrow AI risks. As enterprises rapidly adopt AI to enhance efficiencies, security teams often lack the necessary tools, data, and automation to demystify the complexities of AI. Manifest AI Risk offers a comprehensive transparency and governance solution that highlights vulnerabilities, provenance, software dependencies, and legal risks associated with AI models and their training data.
The AI Risk module includes enterprise capabilities for continuous monitoring, inventory management, and reporting across an organisation’s entire AI infrastructure. As AI adoption outpaces the industry’s ability to secure it, manual AI governance creates strategic bottlenecks that hinder competitive advantage. Executives require business-focused AI risk intelligence to make informed deployment decisions without the burden of technical complexity. Manifest effectively eliminates these bottlenecks, allowing executives to assess AI risks in mere minutes rather than weeks. The market adoption of the Manifest Platform underscores the urgent need for AI transparency, as it currently safeguards over $100 billion in annual defence contracts while collaborating with Fortune 500 organisations across various sectors, including automotive, healthcare, and aerospace. The Manifest AI Risk module was developed through design partnerships with six Fortune 500 companies, ensuring that it addresses real-world AI governance challenges at scale.