AiThority Interview with Tom Findling, CEO of Conifers.ai

Tom Findling, CEO of Conifers.ai talks about the current state of security ops and how AI is impacting modern security ops workflows and investigations in this AiThority interview:

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Hi Tom, tell us about Conifers.ai and the story behind the platform.

After IntSights, my former company, was acquired by Rapid7, I had the opportunity to work closely with the Detection and Response practice. It became clear that traditional security operations models weren’t keeping up with the scale or complexity of modern cyberthreats. SOC teams were overwhelmed by alert fatigue, complex tools, and rigid workflows. Meanwhile, attackers were getting faster by using automation and, more recently, generative AI.

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That experience inspired me to found Conifers.ai. Our mission is to help SOC teams become significantly more effective and efficient through AI and data science. We designed Conifers.ai to empower defenders—not replace them—with a platform that continuously learns and improves results, not just automates.

How has the platform evolved?

We started with a bold idea: traditional SOAR tools are complex and too rigid to handle today’s dynamic threats. They rely on static logic and manual upkeep and assume scenarios can be captured in playbooks, which doesn’t reflect how real SOCs work today.

We built the CognitiveSOC™ platform to move past that. It doesn’t just automate workflows—it learns from each organization’s environment and adapts. Over time, we’ve added features like multi-tiered investigation support, seamless integrations with existing tools, and a staged rollout model that allows enterprises and managed security service providers (MSSPs) to build trust as they adopt AI incrementally. The result is a platform that provides both speed and precision while delivering measurable ROI with a disruptive pricing model.

When it comes to security ops, how is AI changing the game today?

Security ops are being forced to adapt fast because adversaries have not hesitated to take advantage of AI themselves. Generative AI helps create hyper-targeted phishing emails, automate reconnaissance, and generate malware. If defenders don’t learn to fight AI with AI, they are at risk of compromise.

Within security operations, AI is changing how analysts run investigations. What used to take hours can now be surfaced in minutes with contextual insights and automated enrichment. Instead of spending cycles on low-level alerts, teams can focus on incidents that require human insight. This enables a more proactive model of detection and response. AI helps SOCs to identify threats earlier in the kill chain, reduce noise, and speed up investigation times.

What thoughts do you have for the future of AI and security ops?

The future of security operations isn’t about working faster. It’s about working smarter. SOCs will increasingly evolve from reactive alert management hubs into proactive risk reduction engines. AI will handle the repetitive, noisy tasks—surfacing the real signals—so human analysts can focus on strategic, high-value decision-making.

The most successful teams will treat AI as a strategic partner, not just an automation layer. As attackers and defenders leverage AI, the edge will go to those who refine their detections and prioritize quality over quantity.

When using AI to drive security ops processes, what should modern IT and security teams keep in mind?

Adopting AI isn’t an all-or-nothing decision. A phased “crawl-walk-run” approach works best. Start with high-impact use cases for which AI can show immediate value. Build trust in the technology and expand gradually.

Integration is another key. AI must work with your existing SIEM, EDR, IAM, cloud, and ticketing tools to truly add value. And remember: Success isn’t about how many alerts you close; it’s about the risk you reduce. AI gives teams the KPIs and reporting tools to demonstrate that impact internally and externally.

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What are five of the biggest challenges modern IT and security teams still face when deploying new AI solutions for security?

Trust and explainability are at the top of the list. If analysts don’t understand how AI systems make decisions, they won’t rely on them when it counts. The second challenge is integration. AI needs to work across existing tools and legacy environments, or it becomes siloed.

Scaling without sacrificing accuracy is another issue. Balancing fast incident response with quality threat detection without introducing false positives or dropping signals is hard. Data quality is also important. Without rich and contextual data, even the best AI models struggle to produce reliable results. And finally, proving strategic value is a challenge—showing that the technology is significantly moving the needle on the strategic KPIs that matter, such as risk reduction.

What are the top skills the future IT and security workforce should develop in an AI-first age?

Security professionals of the future will need to think beyond tool proficiency. AI and data literacy will be important—understanding how models work and how to validate their outputs. Analysts will also need deeper behavioral analysis skills to interpret complex patterns and adversary techniques.

Technical fluency around integrations and APIs will be increasingly valuable as environments become more interconnected. However, critical thinking still carries the most weight.

Lastly, strong business communication skills will only become more important. Translating technical findings into business risk is how security teams earn trust and influence.

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