How Artificial Intelligence (AI) Can Transform the Security Operations Center?

Gone are the days when AI was considered a future technology. It is now used as part of routine work, helping organizations make precision-based decisions.

The advancement of AI and ML has introduced new emerging cybersecurity threats. The risk is imminent due to the vulnerabilities existing in AI-based systems and the widespread use of AI tools by cybercriminals and malicious actors. There is a laundry list of potential attacks, including advanced phishing attacks, social engineering attacks, deepfakes, AI-led brute force attacks, and voice cloning. Many state actors use AI techniques and leverage large language models (LLMs) to enhance their scripts to perform advanced-level attacks and target critical infrastructure. Not only this, but AI is also widely used to circumvent edge security and evade security controls, facilitating large-scale attacks.

It’s high time to rethink your current strategy for protecting your infrastructure and IT ecosystem. This change requires envisioning the assessment of vulnerabilities, including privilege access, onboarding of human and non-human identities, proactive threat hunting, detection mechanisms, and responding to incidents as they occur. Undocumented and reactive approaches are insufficient in a rapidly evolving IT world, where attackers can quickly adapt and evolve.

The AI and ML-powered SOC can not only detect incidents but also proactively mitigate risks within the organization. It is a proactive yet adaptive approach to identifying and mitigating threats and risks by reducing mundane tasks and solving complex puzzles related to search queries, threat hunting, providing contextual threat intelligence, and enabling humans with intuitive information on actions for specific incidents. This is the more effective way to handle AI-led, sophisticated attacks at machine speed.

In the entire gamut of AI or agentic AI, SOC can’t be fully automated; it always requires human judgment, tribal knowledge, and experience, which are quintessential in analyzing and responding to security incidents. While AI has benefits, it may also bring risks to the organization if the configured use cases and playbooks are not tested properly. That doesn’t mean we should not use it; we should consider it with great sense, but use it after testing.

At Zensar, we have an extensive library of vertical-specific use cases and playbooks that are adaptive to handle AI-led attacks while identifying IOC and IOA in the environment. We follow a mature framework to test the use cases and playbooks in a lab environment. We closely monitor the output and review the pattern to make it available for production use.