Conference Description
Key Takeaways
- Two-day summit addressing the convergence of data security, artificial intelligence, and enterprise governance
- Designed for CISOs, data officers, compliance leaders, and senior IT executives across finance, healthcare, and technology sectors
- Practical focus on operationalising AI security programmes, Data Security Posture Management (DSPM), and regulatory compliance
- Features workshops, certification sessions, roundtables, and CPE credit opportunities
- Sponsors include Cyera, AWS, Deloitte, Snowflake, Microsoft, McKinsey, PwC, and Cohesity
Introduction
The New York 2026 DataSecAI Regional Summit convenes data security and artificial intelligence practitioners for two days of intensive discussion on protecting enterprise data assets whilst enabling responsible AI adoption. Held at the Marriott Marquis Times Square, the summit targets senior technology leaders grappling with the operational realities of securing AI systems at scale. As organisations accelerate their AI deployments, the event addresses a pressing industry challenge: how to balance innovation velocity with robust data protection and regulatory compliance.
The timing reflects broader market pressures. Enterprises across financial services, healthcare, and technology are deploying AI capabilities faster than their security frameworks can adapt. Data governance teams face mounting complexity as sensitive information flows through machine learning pipelines, third-party models, and cloud infrastructure. This summit positions itself as a practitioner-led forum where security leaders can share operational experience rather than theoretical frameworks.
About the DataSecAI Regional Summit
The DataSecAI Regional Summit operates as a community-driven gathering curated by an executive governing team drawn from industry practitioners. Unlike vendor-dominated conferences, the programme emphasises peer-led sessions where attendees learn from organisations that have already navigated the challenges of AI security implementation. The format combines thought leadership presentations with hands-on workshops, certification opportunities, and structured networking.
Participants can earn Continuing Professional Education credits through designated sessions, adding professional development value for security practitioners maintaining certifications. The summit also includes a Partner Bootcamp component, providing deeper engagement with technology providers and their approaches to data protection in AI environments.
Data Security Posture Management and AI Governance
A central theme running through the summit programme is Data Security Posture Management, commonly abbreviated as DSPM. This discipline has gained prominence as organisations recognise that traditional perimeter-based security models fail to address the distributed nature of modern data environments. DSPM platforms provide visibility into where sensitive data resides, how it moves across systems, and whether appropriate controls are in place throughout its lifecycle.
The connection between DSPM and AI governance is increasingly critical. Machine learning systems require vast quantities of training data, often drawn from production databases containing customer information, financial records, or protected health information. Without comprehensive data discovery and classification capabilities, organisations cannot reliably prevent sensitive data from entering AI pipelines inappropriately. Sessions at the summit explore how security teams are building what practitioners describe as trusted data layers—curated, governed data environments specifically designed to feed AI systems without exposing the organisation to compliance violations or data breaches.
The governance dimension extends beyond technical controls. Effective AI security programmes require clear policies defining acceptable data use, model training boundaries, and accountability structures. Several sessions address the organisational challenges of establishing these frameworks, including securing executive sponsorship, aligning security and data science teams, and measuring programme maturity over time.
Operationalising AI Security at Enterprise Scale
Moving from AI security strategy to operational reality presents substantial challenges that the summit directly addresses. Many organisations have developed policies and frameworks on paper but struggle to implement them consistently across business units, cloud environments, and third-party relationships. The gap between security intent and security practice often widens as AI adoption accelerates.
Practitioner sessions focus on the mechanics of building and scaling AI security programmes. Topics include securing funding for data intelligence initiatives, integrating security requirements into AI development workflows, and establishing monitoring capabilities that detect policy violations before they become incidents. The emphasis throughout is on actionable guidance drawn from real-world implementations rather than aspirational best practices.
Risk management frameworks receive particular attention. As AI systems make or influence consequential decisions, organisations face new categories of risk including model bias, data poisoning, and adversarial attacks. Security leaders must assess these risks alongside traditional concerns about data confidentiality and integrity, often without established benchmarks or industry standards to guide their analysis.
Regulatory Pressures and Compliance Considerations
The regulatory landscape surrounding AI and data protection continues to evolve rapidly, creating compliance challenges that cut across multiple jurisdictions and industry-specific requirements. Financial services organisations must navigate existing data protection obligations whilst preparing for emerging AI-specific regulations. Healthcare entities face the intersection of patient privacy requirements with the potential benefits of AI-driven diagnostics and treatment recommendations.
Summit discussions address how compliance officers and security teams are adapting their programmes to address AI-related regulatory expectations. This includes developing documentation practices that demonstrate responsible AI use, implementing audit trails for model training data, and establishing governance structures that satisfy regulatory scrutiny. The practical focus helps attendees understand not just what regulators expect, but how peer organisations are meeting those expectations operationally.
Technology Ecosystem and Industry Participation
The summit draws participation from major technology providers and consulting organisations active in the data security and AI governance space. Sponsors including AWS, Microsoft, and Snowflake represent the cloud infrastructure layer where much enterprise AI development occurs. Consulting firms such as Deloitte, McKinsey, and PwC bring strategic advisory perspectives on AI transformation programmes. Specialised security vendors including Cyera, Cohesity, Cribl, Saviynt, Tamnoon, and Zafran address specific technical challenges in data protection, identity governance, and security operations.
Kenvue’s participation signals engagement from enterprise organisations implementing these technologies, providing balance between vendor perspectives and practitioner experiences. This mix of technology providers, consultancies, and enterprise practitioners creates opportunities for attendees to evaluate solutions whilst learning from organisations further along in their AI security journeys.
Who Should Attend
The summit is structured for senior-level decision-makers with direct responsibility for data security, AI strategy, or governance programmes. Chief Information Security Officers and their leadership teams will find relevant content on building and funding AI security initiatives. Chief Data Officers and data governance professionals can explore the intersection of data management practices with AI-specific requirements.
Compliance officers navigating AI-related regulatory requirements will benefit from sessions addressing documentation, audit, and governance frameworks. Technology executives evaluating AI security investments can assess vendor approaches and learn from peer implementation experiences. The executive-level positioning means content assumes familiarity with enterprise security concepts and focuses on strategic and operational challenges rather than foundational education.
The Path Forward for Enterprise AI Security
As artificial intelligence capabilities become embedded in core business processes, the security implications extend far beyond traditional data protection concerns. Organisations must develop new competencies in AI risk assessment, establish governance structures that keep pace with rapid technology evolution, and build security programmes that enable rather than obstruct innovation. The DataSecAI Regional Summit provides a forum for security leaders to accelerate their understanding of these challenges and learn from peers who are actively building solutions. For organisations at any stage of AI security maturity, the practitioner-focused format offers practical value that translates directly to operational improvement.