As seen on Forbes, Nov. 4, 2019
Rapid advances in AI and machine learning are defining cybersecurity’s future daily. Identities are the new security perimeter and Zero Trust Security frameworks are capitalizing on AI’s insights to thwart breaches in milliseconds. Advances in AI and machine learning are also driving the transformation of endpoint security toward greater accuracy and contextually intelligence.
69% of enterprise executives believe artificial intelligence (AI) will be necessary to respond to cyberattacks with the majority of telecom companies (80%) saying they are counting on AI to help identify threats and thwart attacks according to Capgemini. Gartner predicts $137.4B will be spent on Information Security and Risk Management in 2019, increasing to $175.5B in 2023, reaching a CAGR of 9.1%. Cloud Security, Data Security, and Infrastructure Protection are the fastest-growing areas of security spending through 2023. The following ten charts illustrate the market and technological factors driving the rapid growth of AI in security today:
AI shows the greatest potential for fraud detection, malware detection, assigning risk scores to login attempts on networks, and intrusion detection. Supervised and unsupervised machine learning algorithms are proving to be effective in identifying potentially fraudulent online transaction activity. By definition, supervised machine learning algorithms rely on historical data to find patterns not discernible with traditional rule-based approaches to fraud detection. Finding anomalies, interrelationships, and valid links between emerging factors and variables is unsupervised machine learning’s core strength. Combining each is proving to be very effective in identifying anomalous behavior and reducing or restricting access. Kount’s Omniscore relies on these technologies to provide an AI-driven transaction safety rating.