{"id":21010,"date":"2023-03-09T01:11:18","date_gmt":"2023-03-09T01:11:18","guid":{"rendered":"https:\/\/www.riskandresiliencehub.com\/?p=21010"},"modified":"2023-07-20T19:24:02","modified_gmt":"2023-07-20T19:24:02","slug":"ai-tools-to-adopt-for-enhanced-network-security","status":"publish","type":"post","link":"https:\/\/www.riskandresiliencehub.com\/ai-tools-to-adopt-for-enhanced-network-security\/","title":{"rendered":"AI Tools to Adopt for Enhanced Network Security"},"content":{"rendered":"

As criminals become more sophisticated in their methods, traditional methods of defense are no longer adequate. AI systems are capable of quickly recognizing and responding to new threats, and are able to detect anomalies in data that would otherwise go unnoticed. This helps to ensure that companies remain secure and that their data is kept safe.<\/span><\/h2>\n

Additionally, such tools can be used to automate mundane tasks related to cybersecurity, freeing up resources for more complex tasks. AI is the key to staying ahead of the curve and ensuring that organizations are able to remain secure in an ever-changing digital landscape.<\/span><\/p>\n

Per a report by IBM, the <\/span>global average cost of a data breach<\/span><\/a><\/span> is $4.35 million, and the United States holds the title for the highest data breach cost at $9.44 million, more than double the global average. In the same study, IBM found that organizations using artificial intelligence (AI) and automation had a 74-day shorter breach life cycle and saved an average of $3 million more than those without.<\/span><\/p>\n

According to Dick O\u2019Brien<\/span><\/span><\/a>,<\/span> an intelligence analyst at Symantec, \u201cIdentifying malicious files is no longer enough. Instead, we now need to be able to identify malicious patterns of behavior, and that\u2019s where AI comes into its own.\u201d<\/span><\/p>\n

The following are areas where various AI tools are proving very useful in overcoming the persistent challenges in cybersecurity.<\/span><\/p>\n

Data Loss Prevention<\/span><\/h1>\n

AI-powered data loss prevention systems work by monitoring and analyzing network traffic to identify sensitive data that is being transmitted or stored in an unauthorized manner.<\/span><\/p>\n

These systems use machine learning algorithms to analyze the content and context of data transmissions, as well as user behavior, in order to identify patterns or anomalies that may indicate an attempt to exfiltrate sensitive data. When a potential data loss incident is detected, the system can take a variety of actions to prevent the data from being lost, such as blocking the transmission, alerting the security team, or quarantining the data.<\/span><\/p>\n

In other words, AI and ML allow DLP to harness the power of advanced analytics and automation, enabling it to rapidly scan and identify data that is important to the business. The combination of AI and ML makes DLP the ideal solution for finding business-critical data, providing greater accuracy and speed than traditional legacy solutions. This is <\/span>corroborated by Mckinsey researchers<\/span><\/a><\/span>, who posit that \u201cthe future [of DLP] lies in the application of advanced analytics, machine learning, and contextual heuristics and their integration with privacy and reporting solutions.\u201d<\/span><\/p>\n

Endpoint Security<\/span><\/h1>\n

By streamlining the process for identifying required security measures and automatically applying them to devices, organizations can ensure that all of their endpoints remain in compliance with the latest regulations and standards.<\/span><\/p>\n

The use of machine learning in this context helps to reduce the amount of manual effort required to ensure compliance, freeing up resources that can be used for other purposes. This not only helps to improve the security posture of individual organizations, but it also helps create a more secure environment for everyone.<\/span><\/p>\n

Besides compliance, the system environment is further strengthened as it continuously learns and adapts to new threats, making it more proactive in guarding against the latest security risks. All of these features together create a system that is not only capable of protecting itself, but also of improving its security posture over time in a risk-based manner.<\/span><\/p>\n

By continually analyzing patterns in past user behavior, organizations can accurately predict risk levels and take appropriate measures to ensure that only authorized users have access to sensitive systems, ensuring their data remains safe and secure.<\/span><\/p>\n

Identity and Access Management<\/span><\/h1>\n

Identity-based cyber attacks are a leading cause of security breaches and expert predictions for 2023 say <\/span>the trend will continue<\/span><\/a><\/span>. To combat these, organizations need a 360-degree approach to identity and access management powered by AI systems that use machine learning algorithms to automate and improve the processes involved in managing user identities and access to systems and resources. These systems can help organizations to:<\/span><\/p>\n