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.
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.
Per a report by IBM, the global average cost of a data breach 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.
According to Dick O’Brien, an intelligence analyst at Symantec, “Identifying malicious files is no longer enough. Instead, we now need to be able to identify malicious patterns of behavior, and that’s where AI comes into its own.”
The following are areas where various AI tools are proving very useful in overcoming the persistent challenges in cybersecurity.
Data Loss Prevention
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.
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.
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 corroborated by Mckinsey researchers, who posit that “the future [of DLP] lies in the application of advanced analytics, machine learning, and contextual heuristics and their integration with privacy and reporting solutions.”
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.
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.
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.
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.
Identity and Access Management
Identity-based cyber attacks are a leading cause of security breaches and expert predictions for 2023 say the trend will continue. 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:
- Automate user provisioning and de-provisioning processes, reducing the risk of errors and improving the efficiency of these tasks.
- Analyze user behavior and activity patterns to identify potential security risks, such as attempts to access sensitive data or systems without appropriate authorization.
- Detect and prevent identity-based attacks, such as phishing attacks and brute force attacks, by analyzing user login activity and identifying patterns that may indicate an attempt to gain unauthorized access.
- Provide real-time risk assessment and risk-based authentication, enabling organizations to apply different levels of authentication based on the level of risk associated with a particular request.
All these lead up to the introduction of identity-first approaches to zero trust cybersecurity, of course, backed by AI. According to an IAM expert, Eve Maler, the future of AI-powered zero-trust identity management will enable dynamic protection, finer-grained policies, eliminating personal data in access tokens, and continuous verification.
Intrusion Detection/Prevention Systems
Traditional intrusion detection systems (IDS) have long been relied upon as a foundational layer of network security. As the scope of these threats continues to expand, the need for more advanced IDS solutions that can accurately detect and respond to malicious activity has become increasingly apparent.
As such, many organizations are now turning to more sophisticated, AI-driven IDS solutions that are capable of monitoring a wider range of activities and responding to potential threats in real-time.
In the words of cloud security CEO Ariel Zeitlin, “Rule-based IDS will look for known attack behaviors and alert on them… while artificial intelligence (AI)-based IDS will look for deviations from a behavioral model acquired by the AI algorithms.” Such deviations are alerted to human analysts who are then responsible for assessing the nature of the intrusion and the next steps to take, although, some AI systems are programmed to take actions on their own.
Risk and Compliance Management
In the context of cybersecurity, AI-powered risk and compliance management systems analyze network traffic logs, user behavior, and other data in order to identify potential security threats. They are configured to monitor for compliance with relevant laws and regulations, such as the GDPR, and they may also use natural language processing (NLP) techniques to understand and analyze unstructured data, such as emails or reports.
All of these demonstrate a shift towards more proactive approaches to risk management in cybersecurity with the introduction of AI as against traditionally reactive ones. Predictive capabilities such as predictive analytics, AI-driven risk scoring, and anomaly detection are now being leveraged to identify, assess, and mitigate potential risks before they can cause any harm.
That is not to say that reactive approaches are entirely obsolete. They remain part of the risk management cycle, which also includes predictive and integrated activities, according to a report by Deloitte.
While reactive activities are useful for developing the baselines of certain risks, predictive ones analyze trends to identify emerging risks. Ultimately, an integrated approach is the best, since this involves an holistic outlook that allows you to generate report on current risks and developing ones.
By implementing these tools, organizations can improve their ability to detect and prevent security breaches, protect sensitive data, and ensure compliance with industry regulations.
However, it is important to note that no single tool can provide complete protection against all threats, and it is essential to adopt a holistic approach to security that includes a combination of AI tools, as well as traditional security measures.
So, it is highly recommended for organizations to carefully evaluate their security needs and implement a combination of systems that best meet those needs.
When contemplating a migration to an AI-based security tool, implement the tool in stages, testing it out in nominal situations to validate its capabilities. Once the initial testing is accepted, the AI capabilities can be expanded gradually into major data traffic flows, e.g., at web sites, firewalls and other network perimeter access points. In addition to AI, ensure that the rules for firewalls and other perimeter security devices are regularly updated.
Paul Kirvan is an independent resilience consultant and member of the Resilience Association. www.resilienceassociation.orgRecommend0 recommendationsPublished in Communications & Network
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