Name: AI Without the Hype: A Former Health Plan CISO's Reality Check
Start: 2025-12-10T08:59:00Z
End: 2025-12-10T08:59:50.000Z
Location: BrightTALK

AI-360 explores how artificial intelligence is reshaping the enterprise — from strategy and infrastructure to governance and innovation.

Our mission is to give business and technology leaders the clarity and context they need to deploy AI responsibly, securely, and at scale.

We cover the full enterprise AI lifecycle — from the models and platforms driving transformation, to the data pipelines, cloud infrastructure, and compliance frameworks that make it possible. Our analysis examines how organisations are integrating AI into their operations, how governance, risk, and compliance requirements are evolving, and how leadership teams can balance innovation with accountability.

While our primary focus is enterprise AI, we also track select developments in consumer AI, where breakthroughs often foreshadow trends that will soon shape business adoption and public expectations.

Through independent journalism, expert interviews, and industry insights, AI-360 connects the dots between technology, policy, and real-world impact. Our goal is to equip decision-makers with practical intelligence to build, govern, and grow with AI, responsibly and sustainably.

When 40% of security professionals fail to spot deepfakes under test conditions, what chance do your employees have?

Seven practitioners from the front lines—building detection systems, implementing governance frameworks, selling solutions, and cleaning up after attacks—deliver unfiltered reality about deepfake threats targeting enterprises right now.

Speakers: Bahadir "Bob" Yavuz (Global Telco Consult, fraud detection specialist), Alexandra Jorison (Identif.ai, deepfake detection), Ray Ellis (AI Security Lead, multinational FMCG), Richard Mendoza (Senior vCISO, Compass MSP, AIGP certified), Craig Clark (Director, Clark & Company, education/public sector), Aruneesh Salhotra (Founder, Investor,  OWASP AIBOM Project Lead) David Clarke (vCISO, ISO27001-SOC2)

Key topics:
Account takeover economics ($5K-$10K per incident) • Network-layer authentication using signals attackers can't fake • Zero trust principles applied to human identity verification • The Arup $25M case study • Challenge-response protocols for video calls • False positive crisis in detection platforms • Shadow AI governance failures • Resource constraints degrading security team attention spans • Business case realities when proving ROI before incidents • Education sector vulnerabilities including Nudify apps targeting children • Third-party risk from vendors overselling detection capabilities

Seven practitioners who've implemented systems, governed deployments, sold solutions, and handled the aftermath. The unfiltered reality of protecting organizations when seeing is no longer believing.

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The most chaotic deepfake webinar in the world

How quickly can someone create a convincing deepfake of your CEO? Ten minutes. That's not a future threat—it's present reality that most organizations remain completely unprepared to address.

While 72% of recruiters encounter AI-generated CVs and HR departments brace for 2026 as "the year of deepfakes," enterprises continue approaching synthetic media through outdated cybersecurity frameworks that fail to protect against what's actually happening: workplace harassment with no clear reporting path, and biometric authentication systems creating new vulnerabilities rather than eliminating them.

Speakers: Danielle Hopkins, AI Governance, and Stewart Tinson, Project Director at AI-360, 

You'll learn:
• Why detection tools are losing an arms race they can't win
• What verification systems actually work when you can't trust what you see and hear
• How to protect employees from deepfake harassment your HR department has no framework to address
• Why biometric authentication is making your organization less secure, not more

Key topics:
Financial fraud prevention • Workplace harassment response • Biometric vulnerability • Voice cloning threats • Executive exposure management • Multi-channel verification protocols • Fragmented regulatory compliance • Training without creating fatigue • Positive deepfake applications • Organizational governance structures • Children's safety concerns • Platform liability questions

Essential viewing for CISOs, CIOs, CFOs, Chief Legal Officers, and HR leaders responsible for protecting their organizations and people in an era where digital authenticity is no longer the default assumption.

Ten Minutes to Deepfake: Why Your Organization Isn't Ready for Synthetic Media Threats

When 15-20% of SMS one-time passwords fail to deliver, you're not just losing security—you're losing customers. Companies switching to network APIs report 4-5% growth uplift.

SIM swap attacks are up 1,000% in some markets. SMS pumping fraud costs tens of thousands monthly. Authentication delays cause measurable abandonment. Yet enterprises remain stuck on infrastructure everyone agrees is obsolete.

Bahadir "Bob" Yavuz, Head of Products at GTC, and Stewart Tinson, Project Director at AI-360

You'll learn:
• Why SMS delivery rates run 15-20% below submission rates—and what that costs you
• How network APIs like Number Verify deliver verification in 1-2 seconds vs 15-20 for SMS
• The Indonesia model: 200+ million users, 20% improvement, all three telcos collaborating
• Which APIs can achieve both an enhanced quality of security, and quality of service to the end user

Key topics:
Synthetic identity detection • Number Verify vs SMS OTP • SIM swap attack patterns • GSMA Camara and OpenGateway standards • Data quality in fraud detection • GDPR privacy-by-design architecture • Telco collaboration requirements • Real-world deployment case studies • False positive rate management • Network API productization

For CISOs, CIOs, CFOs, and security teams evaluating authentication strategies, this session provides the business case, technical reality, and deployment roadmap for moving beyond SMS.

Mobile Identity vs SMS OTP: 5 APIs could get you there

When cyber incidents at sea mean navigation failure, propulsion loss, or crew death—enterprise security assumptions break completely. Systems can't shut down mid-ocean. IT support is days away. Deepfaked executive calls could trigger physical catastrophe.

Speakers:  Amit Basu (CIO & CISO at International Seaways, 30 years maritime IT, co-founder Professional Association of CISOs, managing 80 ocean-going tankers) and Stewart Tinson (AI-360 Project Director) 

You'll learn:
• Why "graceful degradation" replaces shutdown protocols when ships must keep moving despite breach
• Edge AI: vessels running local decisions on constrained connectivity for weeks
• Out-of-band verification defending against deepfaked executive calls
• How behavioral analytics baselines ship networks then escalates anomalies to shore SOC
• The 18-month phishing explosion—from detectable errors to expert-baffling perfection

Key topics:
Maritime edge computing • Graceful degradation • OT/IT convergence • Crew access management • Nation-state targeting • AI fraud supply chain • Deepfake defense • CISO personal liability • Board communication • Vendor evaluation • Agentic AI threats • Systemic risk from shared models

From 1986 banking automation to Starlink tankers, Basu explains why AI doesn't just expand what we can do—it expands who can do it, compressing time between adversary idea and execution.

Ships Can't Shutdown: Why Maritime Cybersecurity Rewrites Every Rule You Know

When data sovereignty laws mandate that Malaysian data cannot legally touch Indonesian servers even within the same company, conventional AI scaling dies. This is AI security beyond Western playbooks.

Jane Teh operates where theory meets enforcement. With 22 years in cybersecurity including 10 years London-based consulting, she now advises enterprises across Southeast Asian financial services, manufacturing, and telcos where every AI summit centers on one topic: data sovereignty, data sovereignty, data sovereignty.

Speakers: Jane Teh, Founder, VortiQ [x], and Stewart Tinson, AI-360 Project Director

You'll learn:
• Why Chinese AI hardware at 3-5x lower cost than US equivalents changes infrastructure decisions
• How adversarial training teaches AI to think like attackers using synthetic data
• The three-lines-of-defense banking model adapted for AI governance and accountability
• Human in the loop vs human on the loop - when each governance model applies
• Local explanation techniques that satisfy auditors without exposing IP to attackers
• Why Malaysia's 2027 open financing regulations flip accountability to consumers

Key topics:
Sovereign AI architecture with multi-region gateways • Geopolitical model selection balancing cost vs client geography • Fraud detection AI when attackers subtly modify transactions to bypass thresholds • Model poisoning through manufacturing data pipeline injection • Explainability vs security trade-offs in HFT and insider threat detection • creating AI disconnected from current reality • IoT/SCADA integration exposing legacy OT systems

The reality of implementing AI security where geopolitics meets data sovereignty meets AI-powered attackers moving faster than human response time.

Data Sovereignty Wars: Why Southeast Asia's AI Security Playbook Differs from the West

When a Group CISO with 25 years in security says "I've not seen anything that actually works very well" about deepfake detection technology, that's not skepticism—it's prioritization. Christopher Neal runs security for a multi-jurisdictional healthcare organization where one wrong decision in an operating theater has consequences far beyond brand damage. His priorities reveal where enterprise AI security actually matters versus where vendors want it to matter.

SPEAKERS: Christopher Neal (Group Chief Information Security Officer, Ramsay Healthcare—operating hospitals across Australia, UK, France, Sweden, Norway, Denmark) and Stewart Tinson (AI-360 Project Director) 

You'll learn:
• Why deepfake detection ranks low when you're protecting operating theaters, not just bank accounts
• The "99% accuracy problem": why near-perfect AI models create dangerous human complacency
• How to structure AI governance when clinical ethics trump technical controls
• Red flags that separate enterprise-ready AI vendors from consumer products with sales wrappers

Key topics:
Automating SOC analyst grunt work while keeping humans in the loop • Clinical versus administrative AI risk tolerance • Vendor red flags including single-tenant architectures and identity integration failures • Third-party AI risk assessment when transparency stops below US defense scale • Board reporting on AI threats without operational noise • Why generative AI made criminals faster but didn't create novel attack classes • Organizational resilience over individual blame • Measuring security ROI skeptically • Using closed AI models for patient data protection • The medical dosage nightmare scenario when AI confuses milligrams with micrograms • AI governance committee structure separating cyber, legal, privacy, and clinical ethics responsibilities

A healthcare CISO who spent tens of millions at previous organizations chasing 90% CMDB accuracy explains why he's not chasing deepfake detection tools.

Why Your Healthcare CISO Isn't Buying Deepfake Detection: Real AI Security Priorities

How do you convince skeptical CFOs to invest in deepfake detection?  Stewart Tinson sits down with Ofer Friedman from AU10TIX, who reveals a stark reality: 10 billion synthetic identity sets exist for sale—more than Earth's entire population—and fraudsters now have one-click tools to weaponize them at industrial scale.

You'll learn:

How to build the business case for deepfake detection with boards facing regulatory and reputational risk
Why the shift from onboarding to ongoing fraud targeting existing customers is critical
Which device intelligence and network signals actually work beyond visual artifact detection
How to prepare for agentic AI fraud—autonomous systems that launch attacks without human intervention
Why identity verification and cybersecurity are converging into unified defense strategies

Key topics:
The second revolution of AI fraud and fraud-as-a-service platforms • Why humans can no longer spot deepfakes reliably • Geographic attack hotspots across Americas and Asia • Real-time live session attacks as the most challenging threat • How well-crafted synthetic identities operate undetected for years • AU10TIX's 25+ year background bringing airport security standards to digital verification • Agentic AI as the next wave of autonomous 24/7 fraud • Why annual vendor reassessment is now the minimum standard
Whether you're a CISO, fraud leader, or compliance officer navigating EU AI Act requirements, this delivers brutal honesty about the threats you're facing—and what industrial-grade defense actually requires.

The 10 Billion Identity Crisis: Defending Against Industrial-Scale Deepfake and Synthetic Identity Fraud

How do you prioritize tens of thousands of open vulnerabilities when resources are finite and CVSS scores miss critical context?

In this wide-ranging conversation, Stewart Tinson speaks with Stephen Fridakis, former CISO of Oracle Health, Google Health, HBO, and the United Nations, now advising clients across manufacturing, healthcare, and beyond. Stephen introduces the eBar framework—a revolutionary approach to vulnerability prioritization that adds business context, exploitability testing, and real-world risk factors that CVSS completely ignores.

You'll learn:
• Why CVSS classifications are becoming irrelevant without understanding your specific implementation, business impact, and network topology
• How the eBar framework prioritizes vulnerabilities using sensitivity, exploitability, and contextual risk scoring
• Why overlooked medium vulnerabilities often create larger blast radius than isolated critical findings
• How to communicate technical risks to boards using before/after states and operational impact (canceled surgeries vs. abstract data loss)
• Why the CISO role has fundamentally shifted from technical implementation to business enablement and cross-functional leadership

Key topics:
Vulnerability debt accumulation • The critical importance of pen testing over automated scanning • Managing 12-13 overlapping security and privacy frameworks • GenAI security risks including fraud, synthetic identity, and non-human identity management • Healthcare innovation vs. regulatory compliance • EU AI Act vs. NIST frameworks and jurisdiction conflicts • Future CISO role convergence with compliance, legal, and information management

Hard-won wisdom from someone who's secured everything from UN peacekeeping operations to HBO content releases.

Why CVSS is Failing CISOs: And what works instead.

EU Digital Omnibus Part C: Expert Panel on High-Risk AI, Data Protection & Implementation Reality

The EU Digital Omnibus proposes sweeping changes to GDPR and the AI Act—before organizations have finished implementing all the original requirements. The Commission claims $150 billion in annual savings, but critics argue these changes primarily benefit big tech while creating more complexity for enterprises already mid-implementation.

This expert panel examines whether these proposals genuinely simplify compliance or introduce dangerous new uncertainties.

Key Topics:

Special Category Data Processing - All AI systems, not just high-risk, can now process sensitive data for bias detection. Panel debates: Can you fix bias without the data that reveals it? How do you differentiate genuine testing from pretextual collection?

Subjective Data Definitions - The same dataset could be "personal data" for one organization but not another, based on intent rather than objective characteristics. Experts discuss re-identification risks as AI capabilities evolve.

Implementation Timeline Chaos - Should organizations pause compliance work or push ahead despite regulatory uncertainty? Discussion covers the "panic window" risk and why responsible AI principles remain essential regardless of shifting deadlines.

AI Literacy Downgrade - Does shifting from mandatory to "encouraged" training signal Europe's retreat from rights-based governance?

Panel
Kate Ashworth-Brash, Global HR Technology & AI Governance Director at IBM UK Ltd
Josh Gallan, Data Protection Advisor at Bird & Bird
Ilina Georgieva, Specialist Scientist in AI Governance, TNO Netherlands
Seto Adenuga, AI Governance and Ethics Manager, Kainos
Bose Ayokunle, Data Protection Manager, UK Government Department

Essential for DPOs, compliance officers, AI governance leads, legal counsel, and risk managers navigating practical implementation challenges in Europe's evolving regulatory landscape.

The ABC of The EU Digital Omnibus- Part C.

The EU is proposing to delay and simplify the AI Act before it's fully implemented. Is this responsive governance for fast-moving technology, or undermining a regulation that's "barely started"? This expert panel examines whether compliance should depend on voluntary standards, what happens when AI literacy shifts from mandatory to "encouraged," and why Germany's proposed Article 15 restrictions may violate existing case law.

Key debates:

Standards vs. Law - Should policy effectiveness depend on voluntary industry standards? When harmonized standards are delayed, who bears the compliance risk? Panelists discuss whether companies have incentives to deliberately slow standardization work and what happens to compliance roadmaps when standards bodies miss targets.

Bias Detection Trade-offs - Can you meaningfully test for discrimination without real sensitive data, or does synthetic data introduce new biases? The panel debates whether allowing all AI providers to process special category data for bias testing genuinely helps or normalizes large-scale collection of previously forbidden data.

AI Literacy Downgrade - Does "encouraged" training work, or are regulators giving up? Practitioners share whether boards fund non-mandatory literacy programs and whether Central/Eastern Europe has resources to foster encouraged training.

Article 15 Restrictions - Germany's push to limit access rights to "data protection purposes only" could prevent employees from using data requests for labor disputes. Can you distinguish data protection purposes from evidence-gathering when someone needs data for both?

Panel
Ray Eitel-Porter, author of "Governing the Machine"
Michael Olanipekun, Senior AI Governance Specialist, JLR
Andreea Mare, Data Protection Officer, Superbet
Nikola Saranov, Data Protection Officer, Valtec
Alexandru Gheorghe, Founder, Data Privacy Consultant. Inperspective Business

The ABC of The EU Digital Omnibus- Part B

The EU Digital Omnibus: Simplification or Regulatory Rollback?

The Commission promises €150 billion in annual savings. Max Schrems calls it "the biggest attack on Europeans' digital rights in years."

Part A of our "The ABC of the EU Digital Omnibus" series features three expert practitioners:

Dastan - AI governance advisor to boards and C-suite
Lesley Holmes - Data Protection Officer, MHR International  
Danni - AI governance specialist across education, tech, and public sectors

Key Issues:

SME Exemptions- Lesley: Removing AI literacy requirements "opens the door for small and mid-cap cowboys." Dastan warns: "Large, sophisticated players will arbitrage this ambiguity. Smaller companies will inherit uncertainty."

Subjective Personal Data - Managing data sharing when the same dataset has different legal status for different partners? Lesley: "With difficulty."

Germany's Article 15 Limits - Restricting data access rights could block employment disputes while solving problems that don't exist.

2028 Sandbox Timeline - Dastan: "US companies deploy agent AI today. Waiting for perfect sandbox won't work. Otherwise it's a museum exhibition."

Strategic Reality - Dastan: "Europe is caving in under pressure. EU introduced product regulation without the product."

2026 Actions: Get governance right. Identify shadow AI. Build attestation graphs. Continue as if AI Act fully implemented.

Red Lines: Never deploy AI you don't understand. Never disable AI logging. Maintain universal standards.

Practical guidance for compliance and governance professionals.

The ABC of the EU Digital Omnibus- Part A.

How do you secure hundreds of AI use cases across a global enterprise without slowing innovation? Stewart Tinson sits down with Ray Ellis, who leads AI security for a major multinational manufacturer. Ray shares battle-tested strategies from reviewing over 700 AI use cases, revealing how to balance rapid innovation with robust security—and why data quality remains the most overlooked risk.

You'll learn how to:
- Build AI security programs that enable rather than block enterprise innovation
- Implement risk-based escalation paths giving leadership visibility without bottlenecks
- Secure AI through threat modeling and red teaming adapted for AI systems
- Address data quality challenges and defend against data poisoning attacks
- Deploy prompt injection defenses using technical controls and human-centric guardrails
- Implement continuous monitoring frameworks that catch drift before breaches occur
- Combat deepfake threats through detection technologies and awareness programs

Key Topics:
- Insights from reviewing 700+ enterprise AI use cases and common security gaps
- Why AI security should be "the pointy edge of the sword" not implementation teams
- Risk frameworks mapping 16 key AI security risks to likelihood and impact
- Understanding business processes, actors, and data flows before AI deployment
- Prompt injection explained: direct versus indirect methods and detection strategies
- Whether attackers lead the AI arms race—and why governance may be the differentiator
- Data quality controls as the foundation of responsible AI programs
- Deepfake detection as critical next investment for security teams
- Creative AI security education: gamification and choose-your-own-adventure training
- Building AI security career paths for the next generation

Whether building your first AI security program or scaling globally, this delivers frank, practical guidance from someone navigating the complexity firsthand. Learn to secure AI while maintaining innovation speed.

Securing AI at Scale: Practical Strategies for Enterprise AI Security Implementation

How do you protect your organization when your suppliers' suppliers become your biggest vulnerability? In this interview, Stewart Tinson, Project Director of Newton Media's AI-360, sits down with Amyn Sarif, a leading third-party risk management expert, to explore the cascading risks that emerge when AI-powered attacks and quantum computing converge with complex supply chain dependencies. Amyn shares practical strategies drawn from recent breaches at Jaguar Land Rover, Salesforce, and Volvo Group, revealing why continuous monitoring and automated assessments are no longer optional.

You'll learn how to:

- Map your entire vendor ecosystem beyond direct relationships to fourth and fifth-party suppliers
- Implement continuous monitoring systems that detect breaches in real-time rather than months later
- Automate vendor risk assessments to move beyond manual spreadsheets and annual reviews
- Prepare your cryptographic assets for quantum computing threats before they become exploitable
- Build effective incident response playbooks through tabletop exercises and breach simulations
- Leverage AI defensively while defending against AI-powered credential theft and phishing attacks

Key Topics Discussed:

- Third-party risk versus supply chain risk management
- Recent supply chain breach case studies and lessons learned
- AI's role in both offensive attacks and defensive security strategies
- Post-quantum cryptography transition planning
- DORA compliance and continuous vendor assessment requirements
- Automation strategies for scaling third-party risk programs
- Industry collaboration and threat intelligence sharing

Whether you're managing InfoSec, AI security, or enterprise risk, this conversation delivers actionable strategies to identify hidden supply chain vulnerabilities before attackers exploit them.

Third-Party Risk in the Age of AI and Quantum: Protecting Your Supply Chain from Emerging Threats

How do you scale generative AI inside the enterprise without exposing sensitive data or introducing hidden risks?

In this interview, Stewart Tinson, Project Director of Newton Media's AI-360, sits down with Milan McGraw, Head of Generative AI & Machine Learning at AWS, to break down what every organisation needs to know before deploying AI at scale. Milan shares practical, real-world strategies for securing AI systems — from first pilot to full production — and explains how AWS Bedrock enables private, controlled, and compliant foundation model usage.

You’ll learn how to:

Prevent sensitive data from leaking into models or external systems
Apply least-privilege access and robust identity controls
Build guardrails that protect both users and the organisation
Stand up effective AI governance and risk management programmes
Balance speed of innovation with regulatory and compliance demands
Educate teams across the enterprise to use AI securely and responsibly

Key Topics Discussed:

- Enterprise AI security fundamentals
- Model and data isolation in AWS Bedrock
- Defending against prompt injection in agentic and autonomous systems
- Designing governance frameworks that support innovation
- AI literacy and internal enablement programmes
- Long-term model lifecycle and deprecation planning
- Continuous risk monitoring and posture improvement

Whether you're launching your first use case or scaling AI across a global enterprise, this conversation delivers clear, actionable guidance to help you build AI systems that are secure, trustworthy, and built for growth.

How to Secure Your AI Workloads: Expert Advice from AWS

As AI becomes more embedded in intellectual property (IP) workflows—from patent classification to litigation risk analysis—two questions keep coming up: can we trust what AI is telling us, and how do we stay in control? 

In this live expert panel, we’ll explore how IP professionals are building confidence in AI-driven tools—not just by questioning the tech itself, but by scrutinising the people, processes, and assumptions behind it.

What we’ll cover:

- Trust and validation: What does it mean to trust an AI tool in a legal or strategic context? How do you judge the reliability of its outputs? We’ll examine the role of explainability, the impact of training data, and who’s responsible when AI gets it wrong.

- Model fit and use case alignment: Not all AI is created equal. From classifiers to LLMs and transformers, we’ll look at which tools are best suited for specific IP tasks—and why domain-specific design is key.

- Human-AI collaboration: Do IP professionals need to reskill—or just ask better questions? We’ll explore how to adopt AI responsibly, foster trust across teams, and strike the right balance between automation and human oversight.

You’ll come away with a clearer understanding of what makes AI tools trustworthy, how to evaluate them for your workflows, and how to unlock value—without compromising control.

Who should attend?

This session is designed for patent attorneys and IP leaders operating at the intersection of innovation and technology. Whether you’re selecting vendors, guiding clients, or shaping internal strategy, this conversation will help you engage with AI more critically and confidently.

Can’t make it live? Register anyway and we’ll send the recording to your inbox.

Panelists:

Host: Tom Phillips, Editor, World IP Review
Zhaoying Dorothy Du, Head of Legal, Lenovo
Scott Garnick, Head of Legal Transactions, HERE
Arun Hill, Lead Consultant, Clarivate™ Center for IP and Innovation Research

In AI We Trust? How IP Professionals Can Find Confidence in the Age of Algorithms

Why does every CISO who turns on Microsoft Copilot discover broken SharePoint permissions within hours? Why demand 99.9% AI accuracy while accepting 60-70% from humans? And why are security leaders still worried about problems they should have solved two years ago?

Stewart Tinson speaks with Rick Doten, former CISO of a major US health insurance company and advisor to six AI security startups weekly, for an unfiltered reality check. Rick dismantles the mythology that ChatGPT represents "day zero" of AI, explains why organizations confuse agents with agentic AI, and talks about Model Context Protocol utilizing JSON over RCP—service-oriented architecture from 2005.

You'll learn:
• Why AI exposes existing security problems rather than creating new ones
• The critical agent vs agentic AI distinction determining security implications
• How AI code generates 50x volume requiring 5x more scrutiny
• Why 95% project failure rates miss the point about necessary iteration
• What boards actually need: strategy, compliance, security—not technical details

Key topics:
A2A communication expansion • Copilot permission failures • MCP as API gateway • AI-assisted vs vibe coding • Business impact assessment • Bias as human problem • Seven-gated remediation • Formula 1 governance • Education gap • Third-party risk • Maturity progression • Drift vs misalignment

Whether building AI governance frameworks, managing deployments, or advising boards on strategy, this delivers honest perspective from someone who's seen every implementation pattern and rookie mistake.

AI Without the Hype: A Former Health Plan CISO's Reality Check

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AI Without the Hype: A Former Health Plan CISO's Reality Check

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