Cybersecurity programs will grow but continue to lag behind the talent gap’s growth by at least 25 percent. Unfortunately, we don’t expect the acceptance rate to suddenly increase, meaning cybersecurity program growth will lag behind the talent gap by at least 25 percent in the coming year.

Recently, the terms “machine learning” (ML) and “artificial intelligence” (AI) have proliferated the security space. While there is a great deal of potential as to how these technologies can improve your security posture, there is also a lot of hype and misinformation surrounding what machine learning and AI can do today to improve security.

In this white paper, you will discover the most critical things you need to know about applying ML and AI in your security environment. You will also learn to recognize the most significant opportunities and challenges for using ML and AI to improve your team’s ability to swiftly detect and respond to cyber threats.

The manufacturing sector is on the cusp of its own digital revolution. Industry 4.0—as it has become known—will harness the connected sensors embedded in machinery and logistics operations across the supply chain. The Internet of Things (IoT) is also picking up rapid momentum in its support of Industry 4.0.

But there is a downside. Connecting machines in to external information flows means the machines are susceptible to hackers, industrial spies, and even nation states.

In a perfect world, your organisation would staff a 24x7 SOC—and have no trouble finding the budget or talent to do so. But the truth is, most organisations can’t afford a 24x7 SOC. The cost of having well-trained analysts onsite at all times outweighs the benefits.

If your organisation is making do with an informal SOC, you may be facing delays in responding to incidents. You may even fear that incidents are going unnoticed. It’s a dangerous situation.

But there is a solution: building a SOC that automates as much of the security operations work as possible.