Seekr, a pioneer in explainable and trustworthy AI for mission-critical enterprise and government applications, has introduced SeekrGuard — an advanced platform for evaluating and certifying AI models that goes beyond generic benchmarks to deliver transparent, context-specific risk assessments tailored to organizational data, policies, and operational needs.
With generative AI now embedded in two-thirds of organizations according to McKinsey's 2024 State of AI survey, unvetted models pose escalating threats — from biased decision-making to adversarial exploits that undermine national security and enterprise trust. SeekrGuard counters this by empowering users to interrogate models rigorously on their own terms, ensuring deployments are secure, compliant, and resilient.
"When the President released America's AI Action plan it was made very clear that an evaluation ecosystem was needed to prevent National Security risks and ensure America remains at the very forefront of AI. Seekr answers this call with SeekrGuard," said Rob Clark, President of Seekr.
Traditional benchmarks falter in dynamic environments, relying on static datasets that ignore real-world nuances. SeekrGuard bridges this gap through:
This approach not only detects vulnerabilities but also fosters continuous governance, adapting to evolving threats, policies, and business demands in high-stakes sectors like defense, finance, and healthcare.
SeekrGuard integrates seamlessly with SeekrFlow, enabling organizations to build, test, and deploy domain-specific AI agents with full auditability across cloud, on-premises, and edge setups — positioning Seekr as a cornerstone for trustworthy AI at scale.
Seekr is a leader in explainable, trustworthy artificial intelligence built for mission-critical decisions in enterprises, government, and regulated industries. The company provides secure, auditable AI solutions for sectors where transparency, accuracy, and compliance are paramount. SeekrFlow, the proprietary end-to-end AI platform, enables organizations to build, govern and deploy domain-specific large language models and AI agents on their own data across cloud, on-premises, and edge environments.
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