Session: Use AI to Automate Continous Monitoring of Security Threats
Today organizations face an average of over 2,200 cyberattacks daily, contributing to global cybercrime costs projected to reach $15.63 trillion by 2029.
Traditional security monitoring methods often result in high false positive rates, leading to alert fatigue and overlooked threats. Implementing AI-driven solutions can reduce false positives by up to 90% and improve incident response times by 40-60%.
This session delves into AI-powered techniques, such as machine learning for anomaly detection and natural language processing for threat intelligence, which enable real-time threat identification and proactive defense strategies. Organizations adopting AI in their cybersecurity operations have reported a fivefold increase in detection speed and a 30% reduction in security-related costs.
Through real-world case studies, we will demonstrate how AI-driven automation enhances threat detection accuracy, streamlines security workflows, and strengthens overall cyber defenses. Attendees will gain actionable insights into integrating AI with existing security infrastructures to achieve more efficient and effective continuous threat monitoring.
Bio
I'm a Manager from KPMG's Cybersecurity and Technology Risk group with 9+ years of experience in cybersecurity, technology risk, and compliance. I’ve led compliance and security initiatives at clients such as Google, Mastercard, and IBM, leading AI security and compliance programs. I develop Python applications and have authored LinkedIn Learning courses, including Build AI Applications Using Advanced Python Projects (20,000+ learners) and two AI courses on optimizing ITOps and DataOps. My expertise spans AI governance, risk management, and compliance automation. I’ve audited AI systems, designed security controls, and advised leadership on AI risk. I specialize in implementing AI applications in cybersecurity, automation, and financial services.