Cyber threats have become a problem of monstrous size as digital transformation expands the potential attack surface with each new application and device connected to organizational networks. In 2021 alone, cybercrime costs the world more than $1 trillion, with projections estimating 10-fold growth over the next few years. Governments and businesses are desperate for solutions to counter the growing wave of cybersecurity risks.
And one promising approach harnesses the power of pattern recognition of machine learning models. By training algorithms on volumes of data that would be humanly impossible to analyze, AI and ML have the ability to automatically detect anomalies and prevent threats in real time.
To understand why artificial intelligence provides an essential lifeline, it helps to understand the chaotic scale at which digital threats operate today:
- Millions of Malware Varieties: New malware appears at a rate of more than 500,000 per day which makes it impossible to create signatures and patches for everything.
- Billions of login attempts: Credential stuffing attacks that take stolen passwords and try them on different online services have been repelled 100 billion attempts per year.
- Daily mega violations: Even large companies with security budgets in excess of $20 million suffer from regular data breaches like travel technology provider Saber which disclosed details about 38 million travelers in 2020.
- Hyper-connectivity: Workforces now access cloud applications and remote devices from anywhere, while the increase in IoT smart sensors makes infrastructure and manufacturing prime targets. There are many more threat surfaces than IT teams can track manually.
- Well-funded adversaries: Cybercrime has exploded into a trillion-dollar business, with bad actors using resources on a commercial scale. The Russian cybercriminal group evil society He alone stole over $100 million.
This extreme scale of cyber risks threatens essential services such as health, finance, energy networks and…
