There’s no doubt that artificial intelligence (AI) for cybersecurity is surrounded by an incredible amount of hype.
Cognitive intelligence and machine learning have the potential to combat a series of tricky issues facing the industry. Enterprise security leaders are fighting a widening skills gap, a higher volume of sophisticated attacks and a wider attack surface. Cybersecurity AI has enormous potential to identify advanced persistent threats (APTs) and create automation and efficiency in the overworked security operations center (SOC). AI can learn and adapt to detect anomalies from a real-time stream of data on zero-day threats, attempted data breaches or breach events with data loss.
However, AI’s potential for automation isn’t just limited to the enterprise. Machine learning techniques can be used for both attack and defense, and cybersecurity AI should be viewed as a potential attack vector for adversarial machine learning.
What You Need to Know About Adversarial Machine Learning
Security experts are increasingly concerned about the use of adversarial machine learning techniques aimed directly at cybersecurity AI in the enterprise. Deepfakes, or AI-generated social engineering attacks involving video and audio files, were at the top of MIT Technology Review‘s 2019 list of emerging cyberthreats. Highly convincing media files can now be “pulled off by anyone with a decent computer and a powerful graphics card,” wrote Martin Giles, the San Francisco bureau chief of MIT Technology Review. Deep ..