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Episode 253 – Priya Gnanasekaran, Senior Security Engineer at LAB3 (Australia)

Can AI be both a risk and a defense? In this episode, Priya Gnanasekaran shares how organizations can manage today’s most pressing cybersecurity challenges.

On The Data Diva Talks Privacy Podcast, Debbie Reynolds, “The Data Diva,” speaks with Priya Gnanasekaran, Senior Security Engineer at LAB3 (Australia), about the complex challenges cybersecurity leaders face with AI, IoT, and cloud security. Drawing on her decade-long career spanning DevSecOps, engineering, and operations, Gnanasekaran explains why cybersecurity cannot be reduced to a single field but must be understood as an amalgamation of multiple interconnected disciplines. She highlights the distinction between IT and cybersecurity and explains why this distinction is crucial for executives making risk and investment decisions.

The conversation examines AI’s dual role in cybersecurity, acting both as a new attack vector and as a defensive tool that, when used responsibly, can strengthen organizational security. Gnanasekaran also details the risks of shadow AI and unmonitored enterprise use, exposing businesses to unmanaged vulnerabilities. She addresses weaknesses in IoT ecosystems, including outdated devices and hardware flaws, and argues that these cannot be solved through patchwork responses. Instead, she emphasizes the importance of “shifting left” by embedding security earlier in DevSecOps processes. Gnanasekaran stresses that cybersecurity cannot be treated like a fire department that responds only after damage has been done.

This discussion offers valuable lessons on resilience, innovation, and proactive strategy, applicable not only to security professionals but also to anyone interested in understanding how digital systems can be better protected and managed.

Hosted by Debbie Reynolds, “The Data Diva,” bringing global leaders together on privacy, cybersecurity, and emerging technology.

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🎙️ Episode 252 of The Data Diva Talks Privacy Podcast – J. Mark Bishop and Debbie Reynolds, The Data Diva, on AI myths, GDPR safeguards, and energy costs of large models

In this episode, I speak with J. Mark Bishop, Professor of Cognitive Computing Emeritus at Goldsmiths, University of London, and Scientific Advisor to Fact360, about the myths and realities of artificial intelligence.

Our discussion begins with how we describe AI itself. Mark challenges the language we use, terms like “learning” in machine learning, and argues that much of what is happening is, in fact, just optimization. We examine how anthropomorphic language about AI can create misplaced expectations, shaping how the public and policymakers perceive these technologies.

We examine the tension between AI and privacy, particularly in relation to transparency. Mark reflects on the protections built into frameworks like the GDPR, which explicitly address how personal data may be used when AI makes or informs significant decisions. We examine how these rules strike a balance between individual rights and the need to utilize AI systems in business and government.

Another major theme is metadata analysis. Mark shares insights from his work at Fact360, where analyzing patterns of communication without even looking at message content can reveal signals of organizational change, insider threats, or misconduct. This approach has roots in traffic analysis techniques dating back to World War II, showing how metadata continues to play a powerful role in intelligence and security.

We also discuss the scaling laws of AI and whether building increasingly larger data centers will ultimately lead to artificial general intelligence. Mark strongly critiques this idea, raising concerns about the energy demands of massive AI models. He points out the environmental and ethical costs of data centers, which consume energy on the scale of entire nations, especially when many communities still live in energy poverty.

This episode brings together philosophy, technology, governance, and ethics, a conversation that questions not just what AI is, but what it should be.

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Hosted by Data Diva Media
Debbie Reynolds Consulting, LLC

#AI #ethics #metadata #GDPR #datagovernance #sustainability
#dataprivacy #datadiva #privacy #cybersecurity

🎙️ 🎙️ Episode 258 of The Data Diva Talks Privacy Podcast – Jennifer Wondracek and Debbie Reynolds, The Data Diva, on legal tech, privacy risks, redaction failures, and AI detectors

In this episode, I speak with Jennifer Wondracek, Director of the Law Library and Professor of Legal Research at St. Mary’s University School of Law, about the intersection of law, technology, and privacy in an AI-driven world.

Our conversation explores the digital transformation of law practice, starting with the shift from paper-based systems to electronic filing and research tools, and how today’s wave of AI adoption parallels that earlier revolution. Jennifer explains how lawyers once resisted computers as unnecessary, only to later realize that digital tools were essential for efficiency and productivity. We draw parallels to AI today, where professionals must learn how to integrate new tools responsibly.

We discuss the privacy risks of oversharing data with companies and platforms, and how easy it has become for people to give up personal information without realizing the consequences. Jennifer highlights the duty of lawyers to understand both client confiden

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In episode 251 of The Data Diva Talks Privacy Podcast, host Debbie Reynolds, “The Data Diva,” welcomes Ilia Dubovtsev, Founder of Dub Consulting, joining from Moscow, Russia. he discussion centers on the complexities of privacy in the workplace and how emerging technologies, especially AI, are reshaping the boundaries of personal data and institutional responsibility. Ilia shares his framework for operationalizing privacy—built on the principles of accountability, fairness, and balancing interests, and explains why this model is essential when managing employee data in digitally driven environments.

Ilia shares his belief that privacy is the maximum expression of individual liberty. He developed a three-pillar framework for privacy programs: accountability, balance of interest, and fairness. He explains how these principles can be applied across jurisdictions, whether in Russia, Europe, or the United States. Despite the United States’ lack of a comprehensive federal privacy law, Ilia notes that core principles like non-discrimination and transparency often serve as common ground for workplace privacy protections.

Debbie and Ilia dive deep into the complexities of employee privacy, comparing regulatory and cultural variations. They acknowledge that workplace data, often governed by contract law, labor law, and surveillance practices, is uniquely sensitive because employees have fewer choices about whether and how their data is collected. The conversation shifts to the influence of AI in the workplace. Ilia envisions AI empowering employees by reducing dependency on traditional corporate structures, potentially shifting employer-employee dynamics to a more equitable “peer” relationship. He proposes a new privacy policy model that includes (1) transparent data practices, (2) distinct policy boundaries across employment stages, and (3) accessible remedies for challenging data misuse. Ilia stresses the need for literacy, both technical and legal, to protect workers and hold employers accountable.

The episode concludes with a thoughtful exchange on liberty, trust, and the potential of fair AI governance. Ilia emphasizes that privacy must be preserved through principled regulation and public education, rather than a proliferation of fragmented, burdensome rules. He cites the U.S. scholarly conversation around the “duty of loyalty” and calls for frameworks that ensure both data accountability and empowerment for individuals.

#EmployeePrivacy #GlobalPrivacyFramework #AIinWorkplace #DubConsulting #PrivacyRights #DigitalGovernance #DataLiberty #WorkplaceEthics #AIandHR #PrivacyLiteracy

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