Fighting Fire with Fire: How AI is Revolutionizing Cybersecurity
Malicious actors have weaponized artificial intelligence to launch attacks with unprecedented scale and frequency. In response, more organizations are fighting fire with fire by leveraging AI and machine learning (ML) to enhance their detection, prevention and response capabilities.
With its capacity to adapt and learn, AI is shaping the future of cybersecurity. A whopping 92 percent of companies surveyed recently by Mimecast say they plan to incorporate AI and ML into their cybersecurity efforts, with 49 percent reporting they have already done so.
AI-powered security systems are a force multiplier for resource-strapped IT teams. They can rapidly analyze vast amounts of data, identify patterns and recognize anomalies that human analysts might miss. They also automate many routine security tasks, reducing human error and freeing up staff for other challenges. In addition, AI systems enhance threat intelligence by continuously learning from new data, enabling staff to keep pace with the evolving threat landscape.
Here are just a few of the ways organizations are using AI to enhance cybersecurity:
Threat detection and analysis
AI-powered security orchestration, automation and response (SOAR) platforms ingest and correlate vast amounts of threat intelligence from the network, subscription services and other sources in order to “learn” the difference between normal and suspicious activity. By examining file characteristics, code execution patterns and network behavior, AI algorithms can recognize malicious intent even if the malware is evasive or has not been previously detected.
More organizations are leveraging AI and machine learning (ML) to enhance their cybersecurity.
Real-time threat prevention
Extended detection and response (XDR) solutions continuously collect and correlate real-time security data streams from servers, firewalls, endpoints, cloud instances and other sources. By analyzing the characteristics of known threats, they can proactively identify similar patterns and initiate actions to interrupt attacks before they can cause damage.
Security information and event management (SIEM) systems correlate security alerts with multiple threat intelligence feeds to identify new and evolving threats. Alerts are automatically prioritized based on identifiable characteristics, eliminating much of the time, manpower and expense required to manually comb through large volumes of log data for investigation and response.
Automated incident response
Once threats have been detected, AI can automatically initiate predefined responses such as quarantining affected systems, blocking network access or alerting security personnel.
AI can monitor and analyze user behavior to establish a baseline of normal activities. Deviations such as unusual login patterns or irregular data access requests can be flagged as potential threats. This approach helps identify insider threats, compromised accounts and unauthorized activities that traditional rule-based systems might overlook.
Criminals use compromised credentials to breach critical systems, steal sensitive information, plant malware and launch phishing attacks. AI-powered analytics solutions can detect compromised credentials and trigger remediation efforts.
Deep neural networks can process millions of emails to learn the telltale signs of malicious emails. Machine learning algorithms can even analyze grammar and syntax in texts and emails to spot likely frauds.
AI-enabled cloud access security brokers (CASBs) use behavior analytics to detect anomalies such as excessive downloads or unsanctioned sharing within cloud services that could indicate an active threat. They can also analyze login behavior for the thousands of applications connected to those services to identify additional threats.
GDS Can help you Navigate the AI Landscape
Artificial intelligence has radically changed the nature of cybercrime, but it also offers IT security professionals a growing array of dynamic tools for fighting back with automated threat detection and incident response. Give us a call to learn more about using AI and ML to build a smarter cybersecurity environment for your organization.
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