Cybersecurity has become an important strategic imperative and enterprises today need to monitor and defend their IT assets from the ever-changing cyber threat landscape. All modern enterprises need a robust and comprehensive cybersecurity program to prevent, detect, assess, and respond to cybersecurity threats and breaches. In many ways, cybersecurity is unique – much of detection and monitoring is all about correlation and prediction—and can benefit from the infusion of artificial intelligence and machine learning solutions for assessment, analytics, and automation.
Augmenting cybersecurity with artificial intelligence and machine learning
In a hyper-connected digital world, organizations need to process humongous quantities of data originating from disparate systems to detect anomalies, locate vulnerabilities, and pre-empt threats. Unlike most manual tracking methods, AI and ML-based systems can monitor millions of events on a daily basis and facilitate timely threat detection as well as appropriate and quick response.
AI algorithms are developed based on past and current data to define the ‘normal’ and can identify anomalies that deviate from this ‘normal’. Machine learning can then recognize a threat from these patterns and can also be used to evaluate and classify malware and conduct risk analysis.
An AI algorithm can track and record even the smallest anomaly and has a faster learning curve that better understands and analyzes user behavior. It thus, reduces the workload of security teams which can then focus on incidents that require higher cognitive performance since the algorithms can identify and filter false alarms.
Organizations can also arrest any damage at an early stage by using AI systems to reduce the mean time to detect and the mean time to respond from days to minutes.
Automation of security tasks and processes help improve the overall security posture of an organization and transform itself from being a deterministic enterprise into a cognitive one. It helps in the collection and correlation of security data, detection of existing compromises, and generate and implement protections much more rapidly than humanly possible.
Automation can help with complex security processes in a time-sensitive manner while avoiding manual errors and compliance issues as well as reducing the load on IT resources. It also helps by triggering self-healing processes in case of an attack facilitating quick fixes and the quarantine of injured systems.
Automating mundane and routine security processes can also free up members of the security team allowing them to focus on more strategic aspects of cybersecurity. It reduces their fatigue by keeping them at bay from multiple daily alarms and repetitive tasks like patch management, software updates, identity management, horizon scanning, etc.
Predictive analytics and correlation play a crucial role in cybersecurity and in enabling proactive threat intelligence to help businesses identify security threats before a potential attack.
An organization’s threat intelligence system processes information from diverse global sources, including both commercial and open-source networks. Instead of parsing this information manually each time, artificial intelligence and machine learning can be used very effectively tonot only collect data and insights that can quickly identify potential threats but also to enable a quick response to threats. Even in case of an attack, AI systems can isolate the affected system from the rest of the IT infrastructure to limit the effectiveness of the cyberattack.
Additionally, understanding an attacker’s behavior and having the ability to identify indicators of compromise can also help in detecting incidents and respond to them faster besides making better decisions. Organizations can also tailor the AI and ML algorithms to establish robust systems and processes for self-reporting of security incidents including AI-based behavioral analysis.
While there are several advantages of employing AI for cybersecurity, the advancements in the field have also paved the way for AI-powered cyberattacks and social engineering campaigns by bad actors for corporate espionage, data breaches, financial scams, deepfakes, etc.
Some organizations include ethical hackers as part of the enterprise cybersecurity strategy to beat cyber criminals at their own game. However, using artificial intelligence for brute force verification of AI-powered cybersecurity systems can also lead to an AI model that can outsmart the existing systems for even more advanced cyberattacks.
Not only does artificial intelligence and machine learning help build a robust security framework with always-on risk assessment and coordinate an organization’s incident response, but these systems also work as an automation and orchestration tool to strengthen existing cybersecurity architecture with things like preventive security controls, firewalls and application security, and intrusion prevention systems.
It also helps offset the industry-wide shortage of skilled cybersecurity professionals. As more and more organizations undergo digital transformation, AI and ML can help these modern enterprises build a resilient and future-proof cybersecurity plan instead of traditional methods of tracking, threat detection, and risk assessment.
By Vishal Salvi, Chief Information Security Officer and Head of Cyber Security Practice, Infosys