Businesses around the world are facing record-high numbers of attacks. We are seeing increased occurrences of major businesses being compromised by data breaches, leaving important data on customers, business operations, financial transactions, and much more extremely vulnerable. Security threats are not only growing in numbers but also in complexity and sophistication, which can prove challenging for security teams to keep up with. Artificial intelligence (AI) and machine learning (ML) offer security teams a means of rapidly and effectively managing a broad scope of threats and strengthening their overall security. AI-enabled cybersecurity systems remain highly robust in the face of a constantly evolving cybersecurity landscape.

Businesses are now responsible for more customer data than ever before, and this data is often a target for those seeking to use it for malicious purposes. In particular, personally identifiable information (PII) can reveal information regarding a customer’s identity, behavior, and preferences. Maintaining data privacy is a complex challenge but artificial intelligence can significantly help data management teams in this process. Machine learning can determine how to handle customer data to minimize or even avoid the use of PII. Furthermore, AI can monitor systems for data compliance issues and extract key insights on privacy challenges. Regulatory compliance designed to protect data can vary significantly across regions and they continue to change with a growing focus on data privacy worldwide. AI can autonomously compile data on data security, privacy, and governance changes allowing businesses to develop a holistic data management strategy. Overall, AI allows businesses to build trust with their customers by providing transparency and protection.

Rapid threat detection is a critical part of cybersecurity countermeasures. Machine Learning solutions can autonomously monitor data from cybersecurity systems in real time, detect irregularities in user activity, and flag & intercept potential threats and intrusions. AI tools can adapt to new types of threats and develop new methods to identify them. Machine Learning can play a key role in enhancing security testing efficiency and effectiveness. Traditional threat detection processes require security experts to enumerate as many relevant threat scenarios as possible. AI-based detection improves the scope and breadth of security testing, which allows testing for a significantly larger number of potential threats.

Responding to security threats effectively minimizes damage to business data and computer systems. Security teams have faced a range of threats (low-risk, high-risk, false positives, etc.) and decisions need to be made to decide how to respond to each of them. Accuracy is critical as mistakes can have potentially devastating consequences. Artificial intelligence can help security teams identify appropriate threat resolution measures for each ongoing threat and prioritize responses based on severity. AI automation can intelligently implement cybersecurity countermeasures. Furthermore, it can provide security teams with a comprehensive picture of the overall threat detection and resolution process, which they can use to prepare for future threats.

Security teams use machine learning to analyze historical and live security data to help businesses optimize and prioritize potentially high-risk security threats. ML algorithms can comprehend complex relationships between security parameters and determine the likelihood and severity of potential threats. Furthermore, these algorithms can enable security software to detect threats that standard system scans may fail to identify. ML algorithms can detect malware and spam, malicious system traffic, suspicious user behaviors, and much more. Overall, ML-generated insights can tell cybersecurity teams features, functionality, and strategies they need to integrate to build resilient systems.