“Cybersecurity professionals must curate high-quality training data, fine-tune AIOps models, and provide feedback on missed threats or false positives.”
Akash Shrivastava – Senior Vice President & Global Head – Big Data, Analytics & Artificial Intelligence, Inspira Enterprise.
In today’s data-centric era, organizations continue to accelerate their digital transformation efforts which unfortunately is also expanding the cyber threat landscape. Cyber threats are creating media headlines every day with their growing volume and sophistication, challenging all traditional security measures. Relying on manual processes is no longer sufficient for cybersecurity teams to address the scale and complexity of these threats targeting their organizations. Today, organizations are compelled to adopt cutting-edge cybersecurity technologies to effectively battle the growing cyber threats and secure their sensitive data, applications, networks, and digital assets.
In recent times, businesses have begun to leverage the power of Artificial Intelligence for IT Operations (AIOps) – enabled platforms to implement cybersecurity measures that work. AIOps, which applies AI (artificial intelligence) and machine learning (ML) to automate IT operations, is a powerful weapon revolutionizing the cybersecurity industry. AIOps tools fuse AI and ML to establish AISecOps or AIOps for cybersecurity assisting cybersecurity professionals to not only identify but proactively respond to threats at high speed. The rising complexities of digital networks, talent shortage in the cybersecurity industry, and proactive threat detection capabilities are driving the adoption of AIOps across industry verticals. Furthermore, these platforms use intelligent automation, learn from available data continuously, quickly adapt to changes, and improvise, making them a must-have component of modern cybersecurity strategies.
Leveraging AIOps to Boost Cybersecurity
By integrating AIOps into their cybersecurity strategies, organizations can significantly strengthen their defenses against cyber threats and enhance their overall security posture. The market for AI in cybersecurity, valued at USD 24.3 billion in 2023, is projected to double by 2026 and nearly reach USD 134 billion by 2030, according to Statista. This proactive approach can analyze massive amounts of data, exploring patterns and anomalies to indicate potential security threats or data breaches, and is quite accurate. It is more efficient and cost-effective than managing the aftermath of a cyberattack
Here are the key benefits of AIOps in Cybersecurity
- Automated Real-Time Threat Detection
AIOps platforms utilize advanced machine learning algorithms to swiftly analyze the massive volumes of datasets generated by IT systems in real time, bringing down the vulnerability of new attacks. The platforms continuously monitor the operations of software systems, and quickly identify anomalies and unusual patterns, enabling faster threat detection compared to manual methods. It also sets the base level of normal user activity and quickly detects any deviation equating it to malicious behavior. By automating routine and time-consuming tasks, AIOps allows cybersecurity experts to focus on more strategic and high-level activities. The platform also leverages cyber threat intelligence to generate information that is useful for the cybersecurity teams.
- Enhanced Incident Response
When organizations have to deal with cyberattacks, time is always critical. AIOps helps to automate the initial screening process which otherwise would take significant time for security teams to perform the same task. By automating incident responses, AIOps enables analysts to respond to attacks not only swiftly but also effectively. It proactively identifies anomalies that could be security breaches and responds appropriately before any damage occurs. AIOps platforms can streamline processes, enhance response efficiency, and improve communication between teams, automate incident reporting and communication for more effective incident management. These platforms can isolate affected systems and deploy required patches in record time.
- Reduction in False Positives
Traditional security tools continuously monitor for potential issues and often produce a vast number of alerts, many of which are false positives, contributing to alert fatigue among security teams. The teams have to analyze a huge volume of alerts which is time-consuming, before concluding if it’s a threat, and many a time the attention is diverted from a potential real threat. AIOps platforms manage the alerts effectively by leveraging advanced machine learning algorithms to accurately analyze data, and distinguish between genuine threats and benign activities. By filtering out false positives, AIOps significantly reduces incorrect alerts, allowing security teams to concentrate on real threats. This enhances the efficiency of the SOC and ensures that real alerts are immediately responded to.
- Improved Scalability and Efficiency
As organizations expand their digital footprint, traditional security systems often struggle to keep pace with the increasing volume of data and the growing complexity of IT environments. In contrast, AIOps platforms effectively address this challenge by automating and streamlining cybersecurity operations. Even during large-scale attacks, AIOps can scale rapidly to process vast amounts of data at high speed, ensuring that cybersecurity defenses maintain continuous protection across the entire organization.
- Enables Predictive Analytics
A crucial role is played by predictive analytics in cybersecurity. AIOps significantly boost predictive analytics in cybersecurity by leveraging machine learning to analyze extensive historical data and trends and real-time data. This enables the anticipation and mitigation of risks before they materialize. This proactive approach is very different from the traditional reactive security measures where the focus was on responding to threats after they occurred. By continuously refining their predictive capabilities, AIOps enhances threat detection accuracy and supports organizations in implementing robust preventive measures, ultimately reducing the impact of cyber-attacks.
As organizations face immense pressure to enhance their cybersecurity defenses amidst a shortage of skilled professionals, AIOps has emerged as a powerful tool for security teams. Although AI-driven automation offers significant benefits, effective use of AIOps platforms for security still requires human supervision. Cybersecurity professionals must curate high-quality training data, fine-tune AIOps models, and provide feedback on missed threats or false positives. Additionally, the AI in AIOps must be informed about which applications, services, and resources are critical to the organization. Conducting simulated cyberattacks is vital for testing and improving the platform’s response capabilities. By combining human expertise with AI-driven automation, AIOps platforms can significantly enhance cybersecurity defenses.
AI-driven systems represent the next frontier in threat detection and response, enabling organizations to adopt a more proactive stance in safeguarding their digital assets.