Machine Learning and Cybersecurity: Protecting Your Digital World

Imagine overseeing your entire family's budget, with all their financial transactions popping up on your phone. You may notice unusual or seemingly wasteful expenses, but what if you caught a suspicious transaction? The logical response would be to contact the family member involved for verification, but as you can imagine, this could quickly become a tricky task if you have a big family.

Now, expand your responsibilities to monitor your company's administrative staff and representatives nationwide, along with their expenses - flights, meals, rideshares, and the whole nine yards. And, as if that weren't enough, the companies where your family members work also request your services.

The result? Countless ignored transactions and a significant dent in your sleeping hours. Well, this absurdity is not far from what we see in some companies regarding tasks such as network traffic analysis, endpoint fortification, fraud analysis, authentication security management, user activity analysis, and attack surface management.

Despite the tremendous efforts put into these tasks, they must be more efficiently managed by even the most experienced professionals or well-trained teams. The key here is automation. Automation allows developers to create systems based on well-defined logical rules and procedures. The challenge arises when dealing with non-deterministic behavior, a common characteristic of human behavior.

The solution to managing non-deterministic behavior lies in systems implementing machine learning techniques. These systems have proven to detect anomalies with exceptional accuracy, far surpassing what you could achieve with endless sleepless nights and an overflowing inbox of notifications. However, developing such applications requires professionals with specific knowledge, which is often scarce in the job market. But this is where third-party services come into play.

By integrating your systems and applications with services provided by renowned players like Oracle, Microsoft, Amazon, and Dynatrace, you can ensure that insomnia is not a concern for anyone on your team. Moreover, fewer anomalous behaviors go unnoticed, guaranteeing a more secure environment for you and your users.

So, how can you get started with AI in cybersecurity the right way? Here are some critical steps

• Identify Data Sources and Create Data Platforms: The first step is establishing a consolidated data platform that provides a comprehensive view of your data sources.
• Select Proper Use Cases: Focus on high-benefit, low-complexity use cases such as malware detection, intrusion detection, network risk assessment for operational technology, fraud detection, and user/machine behavior analysis for the Internet of Things (IoT).
• Collaborate Externally: Engage in crowdsourcing platforms to stay updated on the latest threats and enhance AI algorithms for more effective detection.
• Implement SOAR (Security Orchestration, Automation, and Response): Use SOAR to enhance the effectiveness of AI in responding to detected threats rapidly.
• Train Security Analysts: Ensure your security teams are well-prepared to work alongside AI, understanding organizational processes to bolster threat prevention.
• Implement Governance for AI in Cybersecurity: Establish governance to protect AI from compromise and ensure that expected outcomes are met.

In a world where cybersecurity is a top concern, machine learning can be a game-changer in safeguarding digital assets. By following these steps, you can stay ahead of potential threats and ensure the security of your organization and its data.