The threat landscape has moved far beyond programmers trying to show off their exploitative coding skills to their peers. Modern cybercriminals choose efficacy over spectacle and employ a variety of attack methodologies to breach network security. They leverage the most cutting-edge tech to launch swifter, more powerful, and highly sophisticated attacks. With advanced technologies such as machine learning and artificial intelligence now being integrated into cyber attack methodologies, security experts believe that 2018 could be the year that witnesses the first wave of attacks with true AI capabilities.
This spells trouble for global businesses already struggling to deal with high attack volumes and multidimensional attack vectors. With AI now a part of the mix, cybercriminals can introduce greater automation, increasing the speed as well as the surface area of their attacks. If left unchecked, this emerging threat could lead to major data breach incidents, causing unparalleled damage and service disruption on a global scale.
AI-driven Managed Detection and Response
AI’s massive computing power can support, enhance, and sharpen the capabilities of human security experts when it comes to handling large attack volumes and analysing the wide variety of threat data generated at multiple touchpoints. Not only can AI help in shoring up the IT framework to prevent security breaches, but can also aid in proactive identification of potential threats to ensure near-instant response, containment, mitigation, and remediation.
Take threat detection, for instance. Most MSSPs currently manually analyse security data and apply rule-based parameters for identifying incoming threats. Given the sheer number of vulnerable nodes in today’s IT infrastructures, this approach is hopelessly outmoded; since threat data can be generated anywhere across the entire system, cybersecurity experts run the risk of missing a critical alert.
AI-based managed detection and response (MDR), on the other hand, can help security teams in assessing both historical and real-time data from the entire IT stack for more accurate and real-time threat monitoring and breach detection. Using AI-driven MDR also allows for superior threat anticipation which empowers businesses to proactively strengthen their defences against potential attacks, as well as to hunt down threats before security alerts can be generated.
But threat detection and threat prevention are not the only aspects of the MDR approach that AI can augment; it is just as effective in optimising the post-incident response. It can apply real-time rules to security alerts to accurately sort actual security incidents from the data noise and give intelligent recommendations to security teams for swifter threat response. Damage caused by any breach can therefore be contained and mitigated. Detailed forensic insights drawn from previous attacks can also be used to improve cybersecurity strategies, as well as to implement stronger and more robust measures that minimise the risk from similar attacks in the future.
AI in Cyber: A necessity, not a luxury
With around 8.4 billion connected devices currently in use across the globe, the concept of rigidly-defined network perimeters has become a thing of the past. Traditional cybersecurity systems are becoming obsolete, and their prevention-focussed approach cannot provide adequate defence against the complex threats of today. And all this was before cybersecurity experts even started talking of AI-led cyber attacks as an emerging threat.
Countering AI-based attacks will need cybersecurity experts to fight fire with fire. Security solutions driven by artificial intelligence, such as AI-Driven MDR, will have to be incorporated into cyber defence strategies as a standard service for effective cyber defence. AI-Driven MDR is giving defenders the edge in this shadow war against cybercriminals – for the first time in many, many years. For business owners, the only question that needs to be answered is this: how quickly can they get ready for this new paradigm?