Prioritizing data security is more important now than ever before. Today’s cybersecurity threats are incredibly smart and sophisticated. Attackers are using advanced social engineering techniques to trick users into giving away important information or making security mistakes that give the attacker access to their network. Artificial Intelligence (A.I.) and machine learning are beginning to be used for adapting to new and unforeseen attacks from today’s generation of cybersecurity threats, and many cybersecurity experts believe they are fundamental to the future of cybersecurity.

A.I.’s Role in Cybersecurity

Especially with the mass movement to remote work, cyberattacks are increasing in volume and complexity. A.I. is helping security operations stay ahead of threats by curating threat intelligence from millions of research papers, blogs, and news stories. This aggregation of information allows A.I. to provide instant insights to help you fight through the noise of thousands of daily alerts.

Cybersecurity experts are continuing to discover how A.I. can further assist with security, and they are finding promise with machine learning, a type of artificial intelligence that uses the application of artificial technology to allow machines to learn without being explicitly programmed. It focuses on developing computer programs that have the ability to change when exposed to new data.

Through the incorporation of algorithms that learn from data, machine learning allows computers to find hidden figures without being specifically programmed where to look. This is key to allowing cybersecurity systems to carry out human-like tasks and provide first-hand protection. As technology continues to rapidly evolve, machine-learning can be a step in the right direction toward improving cybersecurity.

Currently, A.I. in cybersecurity is used to:

• Detect potential security threats, vulnerabilities, and malicious activities
• Automate security settings and network topography by using network traffic patterns
• Add an extra security layer with biometric verifications
• Avoid human error

Traditional Security Tools

Though it is helpful in cutting through the noise, A.I. cannot and should not completely replace your security system. In fact, some things A.I. cannot replace include:

• Employee training and a security-sensitive culture
• Smart policies and processes
• Qualified architects, managers, engineers, and analysts
• Layered infrastructure with effective controls around it

These are fundamental components of a solid security system that must be kept around. Additionally, you could make the following mistakes when deploying an A.I. cybersecurity tool:

• Expecting a “set-and-forget” solution that will replace the whole security program
• Expecting that the A.I. tool won’t require any customization or integration
• Thinking it’ll all work out on autopilot

A talented cybersecurity team and company-wide awareness training go a long way. A.I. should be seen as a needed support structure that can assist your existing cybersecurity teams in preventing attacks and accelerating mitigation if needed. It is no longer viable to delay the implementation of robust AI cybersecurity tools, but they should add to your current security system, not replace it.

The Future of Security & A.I.

As A.I. becomes more robust, organizations should be able to rely on smart tools to handle event monitoring and incident response. Next generation firewalls will have built-in machine learning technology, allowing them to recognize patterns in web requests and automatically block threats. Future A.I. should be able to track users within an organization based on the roles, rights, and common actions, flagging any deviation from the standard and requiring the use of a second form of authentication.

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