In recent times, there has been marked growth in the volume and complexity of cyber-attacks. Threat actors have begun utilizing AI-power tools to help them develop malicious payloads and launch fast-acting and highly targeted attacks. Such is the sophistication and pervasiveness of these threats that human analysts are now at great pains trying to manage risks manually, and this has left organizations with little option but to seek out more advanced means of defending themselves.
This evolution in the threat landscape has prompted a shift towards more proactive strategies empowered by AI and automation, with recent innovations changing almost all areas of cybersecurity.
Threat detection and response
Security firms have developed a range of new AI-driven platforms that can help organizations improve their threat detection and response capabilities. Security Information and Event Management (SIEM) systems, for instance, can analyze network traffic and endpoint activity to identify suspicious patterns of behavior indicative of threats like malware and ransomware attacks, helping security operations teams to react more swiftly.
Modern threat detection systems can also correlate data from a variety of sources to weed out false positives. This improves the accuracy and efficiency of response measures while simultaneously reducing the workload of analysts. Lastly, these solutions are also capable of identifying software vulnerabilities that require patching, which is becoming increasingly important with threat actors increasingly using tactics like the exploitation of zero-day exploits.
Security testing and validation
One of the biggest challenges for organizations is to ensure that their security controls are actually fit for purpose. Traditionally, this was done through manual testing processes like penetration testing and red teaming, however, this requires considerable downtime, and the periodic nature of these assessments means that they cannot provide the kind of insight modern organizations require. With threats evolving all the time, continuous threat exposure management (CTEM) has become essential, meaning organizations need constant real-time insights into the state of their security posture. This is why automated security validation (ASV) platforms have been developed.
Automated security testing empowers companies to adopt CTEM as a security strategy. They do this by continuously simulating attack scenarios to effectively stress-test security controls and highlight areas of vulnerability. Moreover, they utilize frequently updated threat libraries for breach and attack simulation, employing the latest techniques, tactics, and procedures (TTP) that threat actors are using. This means that with automated security validation software, organizations can continuously check the efficacy of their controls, assess their attack surface, and remedy weaknesses to pre-empt potential attacks, all while reducing downtime.
Threat intelligence
This new generation of AI-powered security solutions does not just enable organizations to prepare for attacks and respond to them. Forewarned is forearmed, after all, so proactivity being the highest tenet of modern cyber defense, it makes sense that they should also empower security operations teams to predict potential threats. In threat intelligence platforms, cybersecurity innovators have managed to do exactly this.
Today’s leading threat intelligence platforms leverage AI and predictive analytics to identify pertinent trends in the cybersecurity landscape and forecast potential threats based on historical information. By aggregating data from a wide variety of global information feeds and industry sources, these platforms can help security teams understand emerging threats so that they may bolster their defenses and update their policies and procedures in a timely manner. This helps organizations to stay hyper-vigilant and ultra-resilient against new attack methods.
Challenges with AI and automated security
The benefits AI and automation offer in cybersecurity are considerable, to say the least, but to take full advantage, organizations must be mindful of tackling some key challenges.
First and foremost, integration with legacy systems needs to be handled with care. Many organizations will have some older security infrastructure in place that may not be compatible with newer AI-powered security platforms. Facilitating the transition to these newer systems will require a strategic approach, particularly with regard to data migration. Biases in AI are something that security teams will need to be conscious of as well, and the utilization of new systems will need to be continually assessed and refined. Lastly, organizations will need to keep the possibility of adversarial AI attacks in mind. With threat attackers adapting their methods to try and circumvent AI-based tools, regular updating, patching, and refinement will be essential to ensuring their long-term efficacy.
Adapting to an AI-driven security landscape
The cybersecurity landscape is shifting, with new threats emerging all the time, and it is abundantly apparent that proactive and adaptive security approaches will be the key to securing sites and safeguarding vital IT infrastructure. Threat actors are leveraging AI for malicious purposes already, but security operations teams can fight fire with fire. By embracing automation in security processes and strategies like CTEM, organizations can seize the initiative in the ongoing fight against cybercrime to ensure that they remain resilient to evolving threats.
