AI in Cybersecurity: How AI Is Transforming SecOps, Threat Detection & Governance

AI in cybersecurity, especially large language models (LLMs), is reshaping the cybersecurity landscape faster than any prior technology shift. Organizations are rapidly adopting AI-driven tools to strengthen AI threat detection, automate analysis, and reduce response times. At the same time, leaders are recognizing that real progress requires more than technology alone: it demands strong AI security governance to ensure responsible, transparent, and compliant use. Together, these shifts are forcing security teams to rethink how Security Operations (SecOps) is structured, measured, and executed.

During a recent Apptega webinar, Chief Product Officer, Rahul Bakshi, was joined by industry leaders David Sampson (Thrive), Ankur Sheth (Ankura), and Joshua Neil (Alpha Level) to discuss how AI is transforming security operations and what organizations should focus on as adoption accelerates. Their collective insights reveal a cybersecurity world on the brink of major change, one that blends automation, human expertise, and emerging governance models.

AI Adoption in Cybersecurity Is Accelerating, But So Are the Risks

Unlike past waves of AI research that stayed confined to data science teams, today’s LLMs are accessible, intuitive, and rapidly entering mainstream workflows. Bakshi noted that this democratization has allowed AI to “help teams do more with less,” pushing adoption into every corner of the enterprise, from operations to marketing to security.

But accessibility comes with responsibility. As organizations experiment with generative AI and automation, the potential for misuse, ethical issues, and inadvertent exposure of sensitive data grows. Bakshi stressed the need for clear guardrails and governance frameworks, comparing today’s moment to the early days of cloud adoption: exciting, full of promise, and still lacking mature standards. Frameworks for Artificial Intelligence Management Systems will structure this next phase, but they’re still in the early stages.  

AI Threat Detection: The New Frontier

AI’s role in threat detection has evolved dramatically over the past two decades. Neil traced the journey from early, script-driven detection methods to SIEMs, advanced endpoint security, and the machine learning models that now enable incredibly high-accuracy malware detection.

But as detection tools have improved, adversaries have adapted as modern attacks rely less on signatures and more on behavior. AI is now being applied not just to identify known threats but to uncover subtle anomalies that indicate suspicious behavior long before damage is done.

Neil warned, however, that while AI supercharges defenders, it also lowers the barrier for attackers. Generative tools can automate exploitation pipelines and enable novice-level threat actors. The challenge for organizations is staying one step ahead.

‍AI Governance Frameworks: The Foundation of Responsible Security

A key takeaway from the panel: AI success starts with governance, not technology.

Sheth emphasized that many organizations rush to deploy AI tools without a clear understanding of the problem they’re trying to solve. Before introducing automation or LLMs into security processes, leadership must define:

• What outcomes they expect

• Which data is safe to use

• Where AI should be allowed or restricted

• How performance and risk will be measured

Data classification plays a critical role. Without knowing which datasets contain sensitive information, organizations risk unintentionally feeding proprietary or confidential data into public-facing AI systems. Tagging, monitoring, and ongoing oversight must be in place long before AI workflows scale.

Human Intelligence Still Matters, A Lot

Despite rapid advancement, the vision of a fully autonomous SOC is still far away. AI excels at pattern recognition, correlation, and scaling routine analysis, but humans remain essential for:

• Interpreting complex attack patterns

• Making risk-based decisions

• Understanding business context

• Responding to zero-day or novel threats

Bakshi emphasized that “AI isn’t magic. We still need the interplay of human intelligence and technology to defend against sophisticated threats.”

Preparing for What’s Next

As AI adoption accelerates, organizations must invest in governance, user education, and continuous learning. Sampson highlighted employee training as a critical first line of defense, especially as generative AI tools become integrated into everyday workflows.

The need for cross-functional alignment is greater than ever. Security leaders, IT teams, and business stakeholders must work together to establish safe boundaries, identify automation opportunities, and ensure AI supports, not replaces, sound security practices.

AI is reshaping cybersecurity, faster, broader, and more deeply than any technology before it. Organizations that approach this shift with clear objectives and strong governance will be best positioned to harness AI’s potential safely and effectively.

Whether improving detection, reducing SOC fatigue, or standardizing risk management, AI offers enormous opportunity. But sustainable progress will depend on striking the right balance between innovation and oversight.