Guardians of the Data | Data Protection for AI: 3-Pillar Framework for CISOs - Areejit Banerjee - Guardians of the Data

Data Protection for AI: 3-Pillar Framework for CISOs - Areejit Banerjee - Guardians of the Data - Episode #33

March 12, 2026 / 37:19/E33

Are your AI models amplifying bias and exposing your organization to data scraping risks without you realizing it?

Today, Ward sits down with Areejit Banerjee, Senior Manager of Data Protection and Product Strategy and published author, to unpack one of the biggest challenges in modern cybersecurity: protecting data in the age of artificial intelligence. With over 20 years of experience across banking, data analytics, AI, and adversarial systems, Areejit shares his perspective on how synthetic data, AI scraping, and outdated policy frameworks are reshaping the security landscape.

Areejit explains how we are reaching the limits of decades of “organic” human-generated data. As that well runs dry, AI systems are increasingly trained on synthetic data generated from existing models. The risk? If the original datasets lacked global representation or contained structural bias, synthetic data doesn’t fix the problem; it compounds it.

Takeaways:

Adopt a Three-Pillar Defense Strategy: Organizations must address data security through technical defenses, product design, and public policy engagement to modernize laws like the Computer Fraud and Abuse Act.
Map Your Scraping Landscape With Common Language: Use standardized ontologies like OWASP's automated threat standards so legal, product, and technical teams speak the same language about threats. This prevents fighting different battles on different grounds.
Classify Data By Value, Not Uniformly: Don't defend all data equally. Identify your data lineage and protect proprietary intellectual property more heavily while keeping commodity-level data more accessible to reduce friction for legitimate users.
Implement Layered Defense Architecture: Create perimeter defenses, protect high-value business assets first, and adjust friction based on customer behavior.
Connect Individual Work to Organizational Mission: Whether you're writing SQL code or building AI models, regularly reflect on how your specific contribution advances the larger mission. This transforms work from bill-paying to meaningful impact and increases job fulfillment.
Conduct AI Impact Assessments Before Launch: Every product using AI should undergo an impact assessment using frameworks from Microsoft, NIST, or the EU AI Act to identify and mitigate bias before deployment, not after problems emerge.
Balance Tactical and Strategic Security Initiatives: Create a day-zero tactical playbook (like onboarding AI-enabled bot detection vendors) while simultaneously planning strategic moves to avoid an endless backlog.

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