Turning Alerts into Answers

Using AI to Rethink Cyber Operations

Rahul Naroola doesn't speak about AI in buzzwords. For the CISO of Dover, a global manufacturing conglomerate, artificial intelligence isn’t a future concept, but a real tool reshaping how security teams work, detect, and respond in real time. From SOC workflows to behavioral anomaly detection and adversarial threats, Naroola sees AI not just as a disruptor, but as a translator: turning complex data into clarity.

He framed the organization’s security operations in terms more commonly associated with business intelligence. It’s a signal of how AI is fusing disciplines and transforming cybersecurity from rule-based alerts into an elastic, adaptive intelligence system capable of scaling with threats and reducing analyst fatigue.

Why Legacy Playbooks Fall Short

Traditional security models, especially those based on static rules or SIEM-based dashboards, no longer cut it. Naroola highlighted the limitations of conventional SOC operations, which require significant manual effort to write queries, tune detection, and filter out noise. "You should be able to say, ‘I want you to tell me when there is a login attempt that happens that's unusual,’" he said. However, under the legacy approach, this simple idea becomes hours of query design and normalization.

That’s where AI changes the equation. "The AI is supposed to elastically figure out what that is," Naroola explained. Rather than analysts writing rigid rules to define what’s normal or abnormal, machine learning can baseline activity and automatically identify deviations. This elasticity is especially powerful when securing a global, heterogeneous organization like Dover, where context and behavior vary widely across environments.

The human bottleneck was not just in logic building, but also in systems navigation. SOC teams often toggle across several dashboards to correlate signals. Naroola envisions a better interface: "Previously, you would see this in movies… but they can quickly just type in a very English-like query and boom, comes up the answer. I don't think we are far away from that."

The Case for a Conversational SOC

Imagine replacing SIEM queries and dashboard configurations with plain language commands. That’s the direction Naroola sees the industry heading, and he welcomes it. Not only does it remove friction for analysts, but it also dramatically reduces the learning curve for junior team members.

Of course, the AI arms race cuts both ways. As defenders automate, so do attackers. Naroola emphasized how threat actors are weaponizing generative AI and automation to enhance social engineering, phishing, and even reconnaissance.

"As AI technology starts to be used, even by criminals… that training would need to evolve," he cautioned. AI-powered adversaries can iterate faster, disguise malicious payloads more effectively, and craft attacks that mirror internal communications with uncanny precision. The threat isn’t hypothetical; it’s already in motion.

“You've gotta be able to leverage the same set of tools,” Naroola said of the defender’s responsibility. He doesn’t see AI as just a defense mechanism, but a necessity for parity.

Rewriting the Analyst's Toolkit

Within Dover, AI isn’t siloed to security. Naroola sees a convergence of disciplines where security analysts, data scientists, and operational leaders all draw from the same intelligence pipelines. "I don't think it's just security. It's going to have a big impact on analytics and how analysis is done across the businesses," he explained.

This convergence also supports his belief that security organizations should embrace their role as analytics engines. “From a SOC perspective, they're running a large data analytics engine anyway,” he said. In this context, AI simply becomes the next evolution of data analysis, used to scale human capability by transforming signal recognition.

He is realistic, though, about the current limitations. “It's not perfect yet,” Naroola admitted. But the trajectory is clear: fewer hard-coded alerts and more pattern-based detection. Fewer dashboards, more conversation. Less fatigue, more insight.

From Queries to Questions

The future of cybersecurity, as Naroola sees it, is rooted in natural language and machine learning. The legacy of keyword queries and complex logic will give way to systems that can interpret intent. This shift won’t just make SOC teams faster; it will make them smarter.

Importantly, Naroola also recognizes the ethical and operational implications. The more decision-making AI is allowed to perform, the more organizations must consider safeguards, explainability, and trust. While this episode focused more on implementation than governance, the subtext of his cautious optimism is clear: leaders must steward this power responsibly.

Across every case Naroola explored, whether it was login anomaly detection, phishing prevention, or streamlined SOC workflows, AI emerged not as an extra layer, but as a core operational model. It empowers smaller teams to operate at scale and makes previously impossible insights routine.

Lessons Learned

Naroola’s vision isn’t built on buzzwords, but is grounded in practical observations, technical empathy, and strategic foresight. In a world where threats evolve at machine speed, the only sustainable defense is to keep up and, in some cases, stay ahead.

By reframing SOCs as data analytics engines, by championing conversational interfaces, and by urging the ethical application of powerful tools, Naroola is building more than just a security program. He’s building a new operational paradigm where AI doesn’t replace the human, but amplifies the human’s ability to protect, perceive, and predict.

"I don't think we are far away," he said. And given Dover’s direction, we should believe him.