Rethinking AI Risk Management as an Enabler
About This Session
Effective governance should accelerate, not hinder, AI adoption.
This session asserts that well-architected risk management does not have to be a barrier for an enterprise, but can instead be the very mechanism that enables a business to move faster, in a secure way. This session will explore how current practices, particularly in Third-Party Risk Management (TPRM), often reveal contradictions: rigorous evaluation frameworks are bypassed when speed is prioritized, exposing a gap between stated risk tolerance and actual behavior.
Additionally, this presentation will highlight the critical but under-examined tradeoff between time investment and risk severity: evaluating whether exhaustive assessments truly reduce AI and security risk.
Attendees will learn:
- Why traditional risk management approaches unintentionally slow AI adoption (and how to fix that)
- Where dread risk and the investigatory reflex impact security decisions around AI
- How cognitive biases like loss aversion, target fixation and salience bias distort AI risk decisions, specifically among security leaders
- A practical method for aligning evaluation effort with actual risk (time and impact evaluated)
- Why assigning clear risk ownership is critical for both speed and accountability
This session asserts that well-architected risk management does not have to be a barrier for an enterprise, but can instead be the very mechanism that enables a business to move faster, in a secure way. This session will explore how current practices, particularly in Third-Party Risk Management (TPRM), often reveal contradictions: rigorous evaluation frameworks are bypassed when speed is prioritized, exposing a gap between stated risk tolerance and actual behavior.
Additionally, this presentation will highlight the critical but under-examined tradeoff between time investment and risk severity: evaluating whether exhaustive assessments truly reduce AI and security risk.
Attendees will learn:
- Why traditional risk management approaches unintentionally slow AI adoption (and how to fix that)
- Where dread risk and the investigatory reflex impact security decisions around AI
- How cognitive biases like loss aversion, target fixation and salience bias distort AI risk decisions, specifically among security leaders
- A practical method for aligning evaluation effort with actual risk (time and impact evaluated)
- Why assigning clear risk ownership is critical for both speed and accountability
Speaker
Keavy Murphy
Vice President - Security - Net Health
Keavy Murphy is a Boston-based security professional currently serving as the Vice President of Security at Net Health. Passionate about cybersecurity and responsible AI adoption, she focuses on securely enabling innovation and aligning risk management with business objectives. Previously, she served in security roles and led global teams at Starburst Data, Cambridge Mobile Telematics, Alegeus, and State Street.
Her work has been published in Dark Reading and Info Security Magazine and presented at seminars including the Chief Data and Analytics Officers Conference, Tech Show London, SecureWorld and FutureCon. She is a volunteer with Boston Cares, has served in the ISACA Mentor program, and holds both CIPP & CIPM certifications.
Her work has been published in Dark Reading and Info Security Magazine and presented at seminars including the Chief Data and Analytics Officers Conference, Tech Show London, SecureWorld and FutureCon. She is a volunteer with Boston Cares, has served in the ISACA Mentor program, and holds both CIPP & CIPM certifications.
