AI coding assistants are generating pull requests with 3x more commits than human developers, creating a code review bottleneck that manual processes can’t handle. Karen Cohen, VP of Product Management of Apiiro, warns how AI-generated code introduces different risk patterns, particularly around privilege management, that are harder to detect than traditional syntax errors. Her research shows the shift from surface-level bugs to deeper architectural vulnerabilities that slip through code reviews, making automation not just helpful but essential for security teams. Karen’s framework for contextual risk assessment evaluates whether vulnerabilities are actually exploitable by checking if they’re deployed, internet-exposed, and tied to sensitive data, moving beyond generic vulnerability scores to application-specific threat modeling. She argues developers overwhelmingly want to ship quality code, but security becomes another checkbox when leadership doesn’t prioritize it alongside feature delivery.  Topics discussed: - AI coding assistants generating 3x more commits per pull request, overwhelming manual code review processes and security gates. - Shift from syntax-based vulnerabilities to privilege management risks in AI-generated code that are harder to identify during reviews. - Implementing top-down and bottom-up security strategies to secure executive buy-in while building grassroots developer credibility and engagement. - Contextual risk assessment framework evaluating deployment status, internet exposure, and secret validity to prioritize app-specific vulnerabilities beyond CVSS scores. - Transitioning from siloed AppSec scanners to unified application risk graphs that connect vulnerabilities, APIs, PII, and AI agents. - Developer overwhelm driving security deprioritization when leadership doesn’t communicate how vulnerabilities impact real end users and business outcomes. - Future of code security involving agentic systems that continuously scan using architecture context and real-time threat intelligence feeds. - Balancing career growth by choosing scary positions with psychological safety and gaining experience as both independent contributor and team player.

When IBM acquired Datastax, they inherited an experiment that proved something remarkable about enterprise AI adoption. Project Catalyst gave everyone in the company — not just engineers — a budget to build whatever they wanted using AI coding assistants. Nic Chavez, CISO of Data & AI, explains why this matters for the 99% of enterprise AI projects currently stuck in pilot purgatory: technical barriers for creating useful tools have collapsed. As a member of the World Economic Forum’s CISO reference group, Nic has visibility into how the world’s largest organizations approach AI security. The unanimous concern is that employees are accidentally exfiltrating sensitive data into free LLMs faster than security teams can deploy internal alternatives. The winning strategy isn’t blocking external AI tools, but deploying better internal options that employees actually want to use. Topics discussed: - Why less than 1% of enterprise AI projects move from pilot to production. - How vendor push versus customer pull dynamics create misalignment with overall enterprise strategy. - The emergence of accidental data exfiltration as the primary AI security risk when employees dump confidential information into free LLMs. - How Project Catalyst democratized AI development by giving non-technical employees budgets to build with coding assistants, proving the technical barrier for useful tool creation has dropped dramatically. - The strategy of making enterprise AI ”the cool house to hang out at” by deploying internal tools better than external options. - Why the velocity gap between attackers and enterprises in AI deployment comes down to procurement cycles versus instant hacker decisions for deepfake creation. - How the World Economic Forum’s Chatham House rule enables CISOs from the world’s largest companies to freely exchange ideas about AI governance without attribution concerns. - The role of LLM optimization in preventing super intelligence trained on poison data by establishing data provenance verification. - Why Anthropic’s copyright settlement signals the end of the “ask forgiveness not permission” approach to training data sourcing. - How edge intelligence versus cloud centralization decisions depend on data freshness requirements and whether streaming updates from vector databases can supplement local models.

What if inertia — not attackers — is security’s greatest enemy? At Databricks, CISO Omar Khawaja transformed this insight into a systematic approach that flips traditional security thinking on its head and treats employees as assets rather than threats. Omar offers his T-junction methodology for breaking organizational inertia: instead of letting teams default to existing behaviors, he creates explicit decision points where continuing the status quo becomes impossible. This approach drove thousands of employees to voluntarily take optional security training in a single year. There’s also Databricks’ systematic response to AI security chaos. Rather than succumb to ”top five AI risks” thinking, Omar’s team catalogued 62 specific AI risks across four subsystems: data operations, model operations, serving layer, and unified governance. Their public Databricks AI Security Framework (DASF) provides enterprise-ready controls for each risk, moving beyond generic guidance to actionable frameworks that work regardless of whether you’re a Databricks customer. Topics discussed: - The T-Junction Framework to systematically break organizational inertia by eliminating default paths and forcing explicit decision-making - Human risk management strategy of moving to behavior-driven programs that convert employees from liabilities to champions 62-Risk AI security classifications of data layer, model operations, serving layer, and governance risks with specific controls for each - Methods for understanding true organizational risk appetite across business units, including the ”double-check your math” approach - Four-component agent definition and specific risks emerging from chain-of-thought reasoning and multi-system connectivity - Why ”AI strategy” creates shiny object syndrome and how to instead use AI to accelerate existing business strategy

Sendbird had AI agents take backend actions on behalf of customers while processing sensitive support data across multiple LLM providers. This required building contractual frameworks that prevent customer data from training generic models while maintaining the feedback loops needed for enterprise-grade AI performance. CISO Yashvier Kosaraju walks Jean through their approach to securing agentic AI platforms that serve enterprise customers. Instead of treating AI security as a compliance checkbox, they’ve built verification pipelines that let customers see exactly what decisions the AI is making and adjust configurations in real-time. But the biggest operational win isn’t replacing security analysts: it’s eliminating query languages entirely. Natural language processing now lets incident responders ask direct questions like ”show me when Yash logged into his laptop over the last 90 days” instead of learning vendor-specific syntax. This cuts incident response time while making it easier to onboard new team members and switch between security tools without retraining. Topics discussed: - Reframing zero trust as explicit and continuously verified trust rather than eliminating trust entirely from security architectures. - Building contractual frameworks with LLM providers to prevent customer data from training generic models in enterprise AI deployments. - Implementing verification pipelines and feedback loops that allow customers to review AI decisions and adjust agentic configurations. - Using natural language processing to eliminate vendor-specific query languages during incident response and security investigations. - Managing security culture across multicultural organizations through physical presence and collaborative problem-solving approaches rather than enforcement. - Addressing shadow AI adoption by understanding underlying problems employees solve instead of punishing policy violations. - Implementing shared responsibility models for AI data security across LLM providers, platform vendors, and enterprise customers. - Prioritizing internal employee authentication and enterprise security basics in startup scaling patterns from zero to hundred employees.

What happens when you scale a crypto company across 160+ countries while maintaining the same security standards as Wells Fargo? At MoonPay, it meant rethinking how traditional banking security translates to high-velocity fintech environments. Doug Innocenti, CISO, breaks down how his team achieved PCI, SOC 2 Type 2, and regulatory licenses like BitLicense and MiCA without slowing product development. The secret is the ability to test multiple security tools in parallel and pivot quickly when something isn’t working. But velocity alone isn’t enough, he cautions Jean. Doug’s approach to AI in security reveals a critical insight: although AI-powered tools can dramatically reduce SOC response times and automate incident analysis, the ”gut instinct gap” remains. His team uses AI to enable faster decisions, not replace human judgment — especially when patterns don’t match what the algorithms expect to see. Topics discussed: - Maintaining bank-level security posture while enabling startup velocity through security-first architecture and platform design principles. - Scaling compliance across 160+ countries using pre-built infrastructure that accommodates PCI, SOC 2, BitLicense, and MiCA requirements. - Implementing parallel security tool testing to accelerate vendor evaluation and avoid bureaucratic delays in enterprise environments. - Adopting next-generation DLP solutions like DoControl that use AI-powered business intelligence for dynamic data boundary creation. - Balancing insider threat monitoring with external threat defense through compensated controls and rapid reaction capabilities. - Managing AI adoption risks while embracing acceleration benefits through defensive technology investment and vendor selection criteria. - Using AI-enhanced SOC and SIEM operations to reduce incident response times while preserving human judgment for pattern recognition. - Building transparent security culture where all employees become security professionals rather than maintaining background security operations.

Myke Lyons brings an unconventional background to cybersecurity leadership, having trained as a chef before discovering his passion for breaking and rebuilding IT systems. As CISO at Cribl, he applies culinary principles like mise en place to security operations while solving the fundamental economics problem facing every security team. The math is unforgiving, he tells Jean: data volumes grow at 28% annually while security budgets remain flat. Myke’s solution involves intelligent data hierarchies that route critical authentication logs to expensive SIEM systems while automatically sending regulatory compliance data to cheaper cold storage, reducing costs by 70-80% through format optimization. Topics discussed: - The fundamental economics challenge of increasing annual data growth versus flat security budgets and how intelligent data hierarchies solve this by routing critical logs to expensive systems while storing compliance data in cheaper cold storage. - Smart data pipeline architecture that eliminates vendor lock-in by enabling simultaneous testing of multiple security technologies on identical datasets while maintaining complete data ownership across any storage platform. - Building security culture through partnership rather than punishment, including automated nudges for personal account security and micro-bonus rewards for completing security training. - AI agent implementation for automated phishing response that performs tier-two-level analysis, hunts across email environments, and provides cohesive incident summaries with risk ratings for security analysts. - The evolution from manual security operations to AI-powered automation, with predictions that full tier one analyst capabilities will be available within months for organizations with comprehensive security telemetry. - Data format optimization strategies that reduce log storage costs by 70-80% through UNIX timestamp conversion and elimination of redundant vendor-specific wrapper formats that create unnecessary data bloat. - Mise en place principles from professional kitchens applied to security incident response, treating procedures like recipes with clear preparation steps and proper tooling to reduce response time and improve consistency. - The importance of establishing data architecture early in security programs to avoid complicated remediation of poor data decisions that become exponentially more expensive to fix over time. - LLM integration for security operations including query writing assistance, pipeline creation, sensitive data redaction, and context-aware threat intelligence that reduces analyst toil and improves detection capabilities.

Welcome to a special edition of Future of Data Security, where our host Jean Le Bouthillier answers the top questions our listeners have asked us. In today’s episode, Jean addresses why 100% data coverage doesn’t equal 100% protection.  Would you like to have Jean answer one of your questions in a future episode? Email podcast@qohash.com with your question and a short summary of why you’re looking for an answer!

Welcome to a special edition of Future of Data Security, where our host Jean Le Bouthillier answers the top questions our listeners have asked us. In today's episode, Jean addresses how data visibility can turn crisis into calm.  Would you like to have Jean answer one of your questions in a future episode? Email podcast@qohash.com with your question and a short summary of why you're looking for an answer!

Robert Kang, Professorial Lecturer of Cybersecurity & National Security, The George Washington University Law School, has been building enterprise cybersecurity programs since 2009, making him one of the “OG” practitioners when most organizations didn’t even have dedicated cyber counsel. His unique perspective comes from protecting both critical infrastructure and social media platforms, highlighting how the same governance, risk management, and compliance framework applies across radically different threat landscapes. In his conversation with Jean, he shares why organizations face equal risks from implementing AI too quickly or prohibiting it entirely, and how complete AI prohibition drives employees to use personal accounts for business purposes, eliminating organizational oversight entirely. Robert’s systematic approach to building relationships with law enforcement agencies before crisis situations emerge provides a practical framework most organizations ignore. From free services like InfraGard to subscription-based programs like the National Cyber Forensics Training Alliance, these partnerships deliver both threat intelligence and confidential channels for sharing information with federal agencies. Topics discussed: - The fundamental differences between protecting critical infrastructure versus social media platforms while using identical governance, risk management, and compliance frameworks. - Why complete AI prohibition creates shadow adoption risks where employees use personal accounts for business purposes, eliminating organizational oversight and control. - Building systematic relationships with law enforcement agencies through programs like InfraGard and the National Cyber Forensics Training Alliance before crisis situations emerge. - The evolution of enterprise cybersecurity legal programs from non-existent in 2009 to essential business functions requiring dedicated counsel and executive sponsorship. - How anticipating technology trends years in advance, rather than reacting to current adoption, positions cybersecurity professionals ahead of emerging threats. - Training methodologies for technology lawyers that combine legal knowledge with technical understanding of AI, cybersecurity, and privacy frameworks. - Essential certification pathways for legal professionals entering technology risk management including CC, CIPP, and AIGP credentials. - Government threat-intelligence-sharing programs ranging from free public services to subscription-based personalized assistance for specific industries. - Why law schools must teach both the law of AI and the technology of AI to prepare students for the transformed legal profession.

Welcome to a special edition of Future of Data Security, where our host Jean Le Bouthillier answers the top questions our listeners have asked us. In today's episode, Jean addresses the fastest way to reduce data security risk.    Would you like to have Jean answer one of your questions in a future episode? Email podcast@qohash.com with your question and a short summary of why you're looking for an answer! Get in touch with your host, Jean Le Bouthillier:  LinkedIn    Listen to more episodes:  Apple  Spotify YouTube 

The healthcare industry’s digital transformation has created unprecedented opportunities for patient care delivery, but it’s also introduced complex security challenges that extend far beyond traditional compliance frameworks. Michael Hensley, Director of Cyber Security at Modern Health, brings a unique perspective to protecting private — and heavily regulated — health data while maintaining the innovation velocity essential for startup success. Healthcare security teams must balance regulatory requirements with business agility, creating frameworks that protect patients without stifling innovation. Michael’s journey from professional musician to software engineer to cybersecurity leader shaped his understanding that effective security programs prioritize people and processes alongside technology investments. His approach demonstrates how healthcare organizations can build security frameworks that enable rather than restrict innovation, creating speedy review processes for new technologies while maintaining rigorous patient data protection standards. His conversation with Jean also explores the evolving landscape of healthcare cybersecurity, from shadow AI risks to the misconceptions surrounding HIPAA compliance. Topics discussed: - The fundamental difference between healthcare cybersecurity and other industries, focusing on real-world patient impact rather than just financial or reputational damage from data breaches. - Common misconceptions about HIPAA compliance, including the regulation’s flexibility and how organizations must interpret general requirements based on their specific business models and patient populations. - How telehealth expansion created new security paradigms, enabling rapid service deployment through cloud-native platforms while introducing risks from easy misconfigurations and third-party integrations. - Shadow AI emergence in healthcare environments where employees seek productivity gains through unauthorized AI tools, potentially exposing patient data to non-compliant platforms without understanding regulatory implications. - Organizational strategies for safe AI adoption in regulated industries, including dedicated review processes, governance committees, and internal tool development that unlocks productivity while maintaining compliance. - The evolution from traditional on-premises healthcare security models to cloud-native architectures where services can be deployed with minimal friction but require sophisticated guardrails to prevent data exposure. - Advanced approaches to vendor risk management in healthcare technology, balancing the need for third-party integrations with rigorous security and compliance vetting processes. - Why effective cybersecurity programs treat people and processes as equally important to technology investments, focusing on ownership models and operational sustainability rather than just tool deployment. - Building security teams that enable business objectives through speedy review processes and treating compliance requests as first-class problems rather than obstacles to innovation.

Welcome to a special edition of Future of Data Security, where our host Jean Le Bouthillier answers the top questions our listeners have asked us. In today's episode, Jean addresses how GenAI is reshaping data security risk.  Would you like to have Jean answer one of your questions in a future episode? Email podcast@qohash.com with your question and a short summary of why you're looking for an answer! Get in touch with your host, Jean Le Bouthillier:  LinkedIn  Listen to more episodes of Future of Data Security:  Apple  Spotify YouTube 

The world of data security has fundamentally changed, yet many organizations still approach it as a one-time project rather than an ongoing journey. In this episode of The Future of Data Security, Orson Lucas, Principal at KPMG, draws on his 20+ years of experience to challenge the ”one-and-done” approach that dooms many security initiatives. After witnessing the evolution from obscure privacy regulations to strategic business differentiators, Orson walks Jean through why even the most sophisticated organizations struggle with fundamental data governance and how the rise of AI assistants is creating unprecedented new risks. Orson discusses why privacy is fundamentally a data governance problem, how to balance comprehensive security with practical investment limits, and why the most effective security strategies build on existing technology ecosystems rather than creating parallel systems. He also shares candid insights about how AI assistants like Microsoft Copilot are changing the risk equation by inheriting user permissions to access sensitive data that humans would never realistically browse through. Topics discussed: - The critical shift from viewing data security as a one-time project to an ongoing journey requiring continuous investment, as threat landscapes constantly evolve even when controls remain static. - Why fundamental data discovery (what you have, where it is, how it flows) remains the most challenging yet essential foundation for effective security, with organizations often attempting to ”boil the ocean” rather than taking a risk-based approach. - The evolution of enterprise security governance structures, with privacy teams increasingly functioning as second-line policy setters while security teams handle operational implementation. - How ”hanging access” creates major security vulnerabilities when departed employees leave behind orphaned permissions with no clear ownership, especially in unstructured data environments. - The emerging risk paradigm where AI assistants inherit user permissions but access far more data than humans realistically would, turning theoretical access risks into actual exposure. - Practical strategies for managing shadow AI by creating internal, managed alternatives that provide similar functionality with proper security guardrails rather than simply blocking innovation. - Why effective security strategies often build upon existing technology investments rather than creating parallel systems, using tools like DLP for broader data discovery purposes. - The limitations of viewing data residency as merely a compliance checkbox, with more sophisticated organizations focusing on broader supply chain integrity and provenance issues. - How balanced security partnerships require understanding stakeholder priorities across legal, privacy, security, data governance and marketing teams to achieve organizational alignment. - Approaches for managing third-party risk as vendors increasingly integrate AI features without proper opt-in controls or transparency about data usage for model training.

The security landscape has radically shifted from ”if you get breached” to ”when you get breached” — and Morgan Stanley’s approach to data protection reflects this fundamental change in mindset. In this episode of The Future of Data Security, Faith Rotimi-Ajayi, AVP of Operational Risk, discusses how sophisticated attackers are now researching and targeting specific financial institutions rather than relying on opportunistic attacks. Faith tells Jean why social engineering attacks have evolved to target entire family units, including compromising newborns’ Social Security numbers for future fraud, and why third-party risk management demands rigorous new approaches as vendors increasingly implement AI without adequate security governance. She also shares her experience implementing dedicated AI governance committees, using risk-based authentication that adjusts friction based on user behavior analysis, and how the pandemic accelerated zero trust implementation by eliminating location-based security models. Topics discussed: - The challenges of maintaining operational resilience against increasingly sophisticated targeted attacks rather than merely opportunistic ones in the financial sector. - The evolution of third-party risk management as attackers now strategically target trusted vendors to gain backdoor access to financial environments. - How AI functions as a ”double agent” in security, enhancing defensive capabilities while simultaneously enabling sophisticated deep fakes and voice cloning attacks. - The emergence of shadow AI and strategies to mitigate risks through dedicated AI governance committees and internal alternative applications. - Why regulatory compliance is an innovation driver rather than an obstacle, using frameworks like GDPR, GLBA, and DORA as baselines for robust security programs. - Implementing security-by-design principles and risk-based authentication that adjusts friction based on context rather than applying uniform controls. - Using user behavior analysis (UBA) and indications of compromise (IOCs) to create security measures that don’t interrupt legitimate user activities. - How the pandemic accelerated zero trust implementation by eliminating location-based security models and forcing more sophisticated endpoint security approaches. - The importance of creating business-aligned data security frameworks that prioritize based on risk exposure rather than applying uniform protection. - Why Faith emphasizes continuous monitoring and testing alongside preventative controls to maintain 24/7 visibility across distributed environments.

”If you aren’t investing in penetration testing, if you aren’t investing in having external auditing and third party reporting like gray and black box type testing, you’re leaving your program extremely exploitable because you’re just admiring the beauty of your own ideas.” This blunt assessment from George Al-Koura, CISO at ruby, encapsulates his refreshingly practical approach to data security. In this episode of The Future of Data Security, George challenges conventional wisdom by predicting a major shift back to controlled data centers as organizations struggle with securing AI implementations in the cloud. He reflects on why no one has successfully created secure LLMs that can safely communicate with the open web, exposes the growing threat of ”force-enabled” AI tools being integrated without proper consent, and explains why technical skills are actually the easiest part of building an effective security team. With threat actors now operating with enterprise-level organization and sophistication,” George also shares battle-tested strategies for communicating risk effectively to boards and establishing security programs that can withstand sophisticated attacks. Topics discussed: - How skills from signals intelligence directly transfer to cybersecurity leadership, particularly the ability to provide concise risk-based analysis and make decisive decisions under pressure. - The challenge of getting organizations to invest in data security beyond compliance standards, while facing increasingly sophisticated threat actors who operate with enterprise-level organization. - The importance of establishing clear leadership accountability with properly designated roles (RACI), investing in appropriate technology, and -implementing rigorous third-party auditing beyond certification standards. - The gradual shift in board attitudes toward cybersecurity as a top-level concern, and how security leaders can effectively articulate business risk to secure necessary resources. - How privacy requirements are increasingly driving security investments, creating a data-centric risk management framework that requires security leaders to articulate both concerns. - The struggle to securely deploy LLMs that can communicate with the open web while protecting sensitive data, paired with the trend of returning to controlled data center environments. - How major platforms are integrating AI capabilities with minimal user consent, creating shadow AI risks and forcing security teams to develop agile assessment processes. - Looking beyond technical skills to prioritize integrity, work ethic, problem-solving ability, and social integration when forming security teams that can handle high-pressure situations.

In this insightful episode of The Future of Data Security, Jean Le Bouthillier speaks with Daniel Maynard, VP of Privacy and Data Risk Management & CPO at Early Warning, shares his journey from law to privacy and offers a practical framework for assessing AI implementation risks — distinguishing between controllable technical risks and more complex model provenance concerns. Daniel tells Jean about the critical challenges facing financial institutions, including data quality issues, AI ethics considerations, and the paradox of balancing fraud prevention with privacy protection. Daniel provides actionable governance strategies for managing shadow AI, addresses emerging threats from AI-powered fraud, and offers valuable insights on the evolving regulatory landscape. His balanced approach emphasizes documented risk assessment processes while acknowledging varying organizational risk tolerances. Topics discussed: - The importance of data quality as a foundation for all other security and privacy initiatives in financial services. - Emerging challenges with AI ethics and trust, particularly regarding data provenance and transparency in model development. - Practical governance frameworks for implementing AI tools while documenting risk-based decision processes with executive buy-in. - Model provenance risks and IP concerns when using AI tools to create potentially valuable intellectual property. - Shadow AI challenges and strategies for managing employee use of AI tools while maintaining appropriate security controls. - File access risks with AI assistants that can search through user-accessible content more thoroughly than humans typically would. - The paradoxical relationship between stronger fraud protections and potential negative privacy impacts from increased data collection. - Predictions about federal AI regulation in the United States versus the more restrictive approach seen in Europe. - Career advice for privacy professionals, including gaining cross-functional experience and maintaining a positive, problem-solving mindset.

Within just four hours of implementing controls at one healthcare organization, Patrick Carter, Sr. Practice Director at Cyderes, and his team caught an employee secretly selling sensitive patient data. Patrick doesn’t just tell Jean his war stories, however — he provides a practical framework for quantifying security risks using the FAIR model and sounds the alarm on shadow AI becoming the single biggest threat to data security. From discovering that 10% of AI-generated code contains vulnerabilities to developing detection tools for unauthorized AI usage, Patrick offers a masterclass in navigating both the dangers and opportunities of AI for security leaders. Topics discussed: - Building a specialized data protection practice from the ground up, with insights into how Patrick scaled his team to 40 consultants while maintaining excellence in service delivery. - The dual challenge organizations face with data security: understanding complex compliance requirements and gaining visibility into what sensitive data exists in their environments, where it’s stored, and how it moves. - Shadow AI emerging as the most significant threat to data security in 2025, with statistics showing 60% of employees using free AI platforms and approximately 10% of prompts containing sensitive data. - Using the FAIR risk model to translate complex security concepts into quantifiable financial impacts that help CISOs make data-driven investment decisions. - A real-world case study where implementing data tagging and DLP controls uncovered an internal data theft operation at a healthcare organization within just four hours of deployment. - The strategic integration of AI into service delivery, including developing an AI agent that functions as a Level 1 data analyst for managed DLP services. - The critical importance of follow-through in professional growth, and how it’s the single most important trait for success in the cybersecurity field.

The cybersecurity landscape is entering an AI arms race, and Kevin Kirkwood, CISO at Exabeam, is on the frontlines building defenses that can match the speed of machine-powered threats. As Exabeam’s ”Customer Zero,” Kevin shares candid insights from transitioning through three platform generations in three years, reflecting on how each migration exposed previously undetected attack patterns in Microsoft environments. His experience leading the rapid adoption of 700+ UEBA rules simultaneously (against recommended practice) offers valuable lessons for security leaders pushing the boundaries of detection capabilities. Kevin envisions a future where AI-assisted systems can propose new detection rules for zero-days within minutes, while grappling with immediate challenges — like the day Microsoft Edge suddenly claimed his company had authorized Copilot without CISO approval — highlighting the complex reality of managing AI tool permissions in enterprise environments. Topics discussed: - The strategic shift from total log collection to intelligent edge filtering, rethinking the ”collect everything” approach while maintaining forensic capabilities through AI-powered agents at the edge. - Specific examples of Microsoft Copilot attempting wholesale access to contact lists and email histories, and tactical approaches to implementing granular controls. - Implementing UEBA at scale, including transitioning from basic logging to behavior analytics capable of detecting subtle ”living off the land” attacks that manipulate normal business functions. - How reframing ”security vulnerabilities” as ”security defects” fundamentally changed developer engagement. - Technical insights into how attackers are using GenAI to transform sophisticated exploits across programming languages, and defensive approaches to match this velocity. - Managing bimodal security architecture and balancing edge-based detection with centralized analysis, including specific identity management challenges in the context of AI tool adoption. - A detailed framework for embedding security professionals within development teams while maintaining the balance between velocity and control. - Technical requirements for near real-time zero-day detection and the evolution toward AI-assisted rule generation.

Drawing on his unique background in high-energy physics experimentation, Robert Roser, CISO & Director of Cyber Security at Idaho National Laboratory, offers valuable insights into the parallels between managing complex scientific detectors and securing critical national research infrastructure. He explores the evolving landscape of scientific computing security, from the open science environment of Fermilab to the classified research world of nuclear energy. Rob’s practical experience implementing zero-trust architecture, managing international collaborations, and navigating federal compliance requirements provides a comprehensive view of modern cybersecurity challenges in sensitive research environments. His candid discussion of AI’s impact on both security threats and solutions, particularly in the context of high-performance computing and shadow AI risks, also offers valuable perspective on the future of data protection in scientific research. Topics discussed: - The transition from particle physics to cybersecurity leadership, highlighting transferable skills in managing complex systems and critical operations. - The evolution of scientific computing security from open science environments to classified research protection at national laboratories. - Implementation of zero-trust architecture for managing diverse international collaborations while protecting sensitive nuclear research data. - The challenges of securing high-performance computing infrastructure while maintaining accessibility for legitimate research needs. - Balancing federal compliance requirements with risk-based security approaches in government-funded research environments. - The impact of AI on both security threats and defensive capabilities, including advanced phishing and automated security operations. - Management of shadow AI risks and unauthorized cloud service usage in sensitive research environments. - Future trends in data protection and infrastructure security, focusing on automation and advanced threat detection. - Strategies for securing remote access while supporting global scientific collaboration and research initiatives. - Career advice for aspiring cybersecurity professionals, emphasizing the importance of diverse experiences and continuous learning.

Drawing from his diverse background in both private and public sectors, Chris Pahl, CPO of the County Executive Office of the County of Santa Clara, tells Jean how organizations can transform privacy from a compliance burden into a strategic asset on this episode of The Future of Data Security Show. Chris’s ”U R IT” framework emphasizes the crucial role of employees in data protection, and his practical approach to managing AI risks and surveillance technologies offers a blueprint for modern privacy leadership. He demonstrates how to build privacy programs from the ground up, foster cross-departmental collaboration, and navigate the evolving landscape of data governance in an AI-driven world, all while maintaining a human-centric approach that puts trust and transparency first. Topics discussed: - Building trust in public sector privacy while balancing transparency with data protection requirements - Transforming privacy from a cost center into a strategic partner that enhances organizational mission - Managing the emerging risks of generative AI while enabling innovation and efficiency for employees - Implementing effective employee surveillance through transparency and clear communication - Evolution of the Chief Privacy Officer role toward holistic data governance and technical expertise - Strategies for measuring privacy program success through integration and cultural adoption - Importance of proactive relationship building and avoiding the ”department of no” mentality - Developing privacy programs incrementally while building cross-functional partnerships