AI

From Compliance to Culture: Rethinking Cybersecurity in the Age of AI

We are living in a paradox. Artificial intelligence is often hailed as the great enabler of digital transformation. However, it is also proving to be a potent weapon in the hands of cyber attackers. Yet on the defensive side, organizations often struggle to extract similar value. Why is it that AI empowers offense more effectively than defense?

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The Quality Imperative: Why Leading Organizations Proactively Evaluate Software and AI Systems with Yiannis Kanellopoulos, hosted by George Anadiotis

In an increasingly AI-driven world, quality is no longer just a technical metric—it’s a strategic imperative. In this episode of George Anadiotis’ podcast series, our CEO Yiannis Kanellopoulos dives into how leading organizations are reshaping their approach to software and AI systems by treating trustworthiness, governance, and performance as first-class priorities.

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ISO 42001 Advisory Form -Evaluate Your Readiness for Responsible AI Governance

As AI systems become more central to business operations, the need for formalized, accountable, and ethical AI practices has never been greater. ISO/IEC 42001 is the first international standard specifically designed to guide organizations in establishing, implementing, maintaining, and continually improving an AI Management System (AIMS). It addresses both the opportunities and risks associated with AI, promoting responsible innovation across sectors.

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Avoiding Common Pitfalls in Performance Testing of AI Systems

As AI becomes more tightly integrated into business operations, powering everything from threat detection to operational optimization, its reliability and real-world performance have never been more critical. Well-documented methods for ensuring AI quality and robustness and structured frameworks for building trustworthy AI systems through rigorous evaluation exist.

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Advanced AI Quality Testing: Ensuring Robust and Trustworthy AI Systems

As AI systems become integral across industries, ensuring reliability, fairness, and trustworthy performance is critical. From healthcare and finance to autonomous driving and customer service, AI systems present unique challenges requiring comprehensive quality testing to manage risks, foster trust, and achieve consistent performance in diverse, real-world applications.

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