Kevin Barona
CEO @ Cycore | Helping Organizations Manage Security, Privacy & Emerging AI Risk
New York City, New York, United States
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Kevin Barona is the founder and CEO of Cycore, a managed service that embeds fractional CISOs and compliance leaders directly into teams to execute security, privacy, and compliance programs. Cycore handles the end-to-end work for SOC 2, ISO 27001, HIPAA, and other frameworks. Before founding Cycore, Kevin worked in cybersecurity at Deloitte and Booz Allen, served as a legislative aide on Capitol Hill, and began his career securing nuclear systems.
Area of Expertise
Topics
Governing AI + Blockchain: Privacy, Risk, and Accountability in Autonomous Systems
As autonomous AI agents begin transacting, executing contracts, and making decisions on-chain, the questions of who is accountable, how privacy is preserved, and how risk is governed have never been more urgent. This panel brings together diverse voices from industry, policy, and academia to examine the fast-emerging intersection of artificial intelligence and blockchain.
We will explore practical governance models for autonomous on-chain systems, the privacy and data-protection challenges of AI agents operating on public ledgers, and the accountability gaps regulators and builders must close before this technology scales. Drawing on real-world experience embedding security, privacy, and compliance leadership into organizations, the panel will move beyond hype to deliver actionable frameworks that founders, investors, and policymakers can apply today.
Attendees will leave with a clearer understanding of how to build, audit, and regulate AI-driven blockchain systems responsibly, and where the greatest risks and opportunities lie in the year ahead.
Who's Accountable When the Agent Acts? Governing Autonomous AI at Work
By 2026, AI isn't just assisting workers, it's acting for them. Autonomous agents now send messages, move money, write code, and make decisions at machine speed. But as AI becomes the fabric of how we work, a hard question surfaces: when an agent acts, who is accountable for what it does?
This session confronts the accountability gap emerging in the agentic era. When software takes actions on an employee's behalf, the lines around responsibility, privacy, and compliance blur, and most organizations are deploying these systems faster than they can govern them. That gap is where real-world risk lives: unauthorized actions, data exposure, and regulatory exposure that surfaces only after something goes wrong.
Drawing on frameworks like the NIST AI Risk Management Framework and the EU AI Act, Kevin walks through practical accountability models for autonomous AI, how to set guardrails, assign ownership, and build human oversight into agent workflows before regulation forces the issue. The focus is on what works in practice, not theory.
Key takeaways:
- Accountability models for deciding who owns the risk when AI agents act
- Emerging regulatory signals every leader should track in 2026
- A practical checklist for deploying autonomous AI safely and responsibly
Who's Accountable When the Agent Acts? Governing Autonomous AI at Work
By 2026, AI isn't just assisting workers, it's acting for them. Autonomous agents now send messages, move money, write code, and make decisions at machine speed. But as AI becomes the fabric of how we work, a hard question surfaces: when an agent acts, who is accountable for what it does?
This session confronts the accountability gap emerging in the agentic era. When software takes actions on an employee's behalf, the lines around responsibility, privacy, and compliance blur, and most organizations are deploying these systems faster than they can govern them. That gap is where real-world risk lives: unauthorized actions, data exposure, and regulatory exposure that surfaces only after something goes wrong.
Drawing on frameworks like the NIST AI Risk Management Framework and the EU AI Act, Kevin walks through practical accountability models for autonomous AI, how to set guardrails, assign ownership, and build human oversight into agent workflows before regulation forces the issue. The focus is on what works in practice, not theory.
Key takeaways:
- Accountability models for deciding who owns the risk when AI agents act
- Emerging regulatory signals every leader should track in 2026
- A practical checklist for deploying autonomous AI safely and responsibly
When AI Outpaces Governance: Building Trust into Automated Systems
In 2025, AI adoption moved faster than any organization's ability to govern it, and that gap is now the single biggest source of unmanaged risk in modern data programs. Teams shipped models, agents, and automated decisions into production long before the guardrails caught up, leaving privacy exposure, compliance blind spots, and eroding trust in their wake. Doing data right in 2026 means closing that gap on purpose, not by accident.
This session lays out a practical approach to governing AI at the speed it's actually being deployed. Drawing on emerging standards like the NIST AI Risk Management Framework and the EU AI Act, Kevin walks through how to identify, measure, and mitigate AI risk before it becomes a liability, and how to embed those controls into the systems and workflows teams already use. The focus is on what works in the field, not theory.
The core insight: governance is not a brake on innovation but the mechanism that lets it scale safely. Attendees will leave with a usable framework for building trust into automated systems, turning "responsible AI" from a compliance checkbox into a durable advantage with customers, regulators, and partners.
Global Data & AI Virtual Tech Conference 2026 Sessionize Event Upcoming
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