As artificial intelligence reshapes the way enterprises communicate, the pressure to ensure compliance in real time has never been greater, particularly in highly regulated sectors like financial services. Traditional compliance models, built on retrospective review and remediation, are increasingly misaligned with a world where both humans and AI agents generate high-volume, high-velocity communications.

ZeroDrift, an AI-native compliance infrastructure platform reimagining how regulatory enforcement works. By encoding frameworks such as those of the U.S. Securities and Exchange Commission (SEC) and Financial Industry Regulatory Authority (FINRA) into machine-readable rulepacks, ZeroDrift shifts compliance from after-the-fact detection to real-time prevention, intervening at the point of composition before a message is ever sent.

In this conversation with AI Spectrum, Kumesh Aroomoogan, Founder and CEO of ZeroDrift, explains how the company’s three-layer architecture, Compose, Guard, and Command, turns policy into executable infrastructure, how it minimises false positives in high-volume environments, and why real-time enforcement is poised to become a foundational layer for enterprise AI deployment. He also outlines a broader vision: a world where every policy is enforced on every action, by every human and AI agent, automatically and in real time.

What specific gaps in traditional compliance workflows inspired the creation of ZeroDrift, and how does your AI-native approach fundamentally differ from legacy systems?

Traditional compliance is backwards. The message gets sent, archived, and reviewed weeks later. By then, the violation already happened. ZeroDrift flips this: we enforce before the message is sent. Real-time, at the point of composition. Not review-and-remediate. Prevent-and-protect.

ZeroDrift encodes SEC, FINRA, and firm-specific policies into machine-readable rulepacks. Can you explain the technical architecture behind this and how the system adapts to evolving regulatory frameworks?

Three layers: Compose (communication policies), Guard (enforcement engine), Command (oversight and audit trails). Rulepacks translate regulations into machine-executable rules. They're modular, so firms layer internal policies on top of regulatory baselines. When regulations change, we update rulepacks and every customer gets the update automatically. Policy as infrastructure, not a human process.

How does ZeroDrift ensure accuracy and minimise false positives or over-blocking in high-volume environments like financial services?

Our rulepacks encode intent and context, not keywords. The system knows "we returned 20 per cent" is factual, while "we'll return 20 per cent next year" is a violation. Compliance teams can tune sensitivity, set escalation paths, and define what gets blocked vs. flagged vs. auto-approved. Compliant communication flows freely. Only real risk gets intercepted.

With AI adoption accelerating, how do you see real-time compliance infrastructure becoming a foundational layer for enterprise AI deployment?

Every enterprise is deploying AI agents that communicate on its behalf. Everyone is a compliance liability. As we move from humans to agents sending thousands of client communications, a real-time enforcement layer becomes existential. ZeroDrift sits between intent and action, whether the actor is human or AI.

How does ZeroDrift integrate with existing tools like CRMs, email platforms, and generative AI systems without disrupting workflows?

We integrate at the communication layer, not the application layer. CRM, email, AI tools: ZeroDrift evaluates in real time and if it's clean, it goes through. One set of rules, enforced everywhere. The experience is: write naturally, stay compliant automatically.

Do you envision compliance automation expanding beyond communications into areas such as transaction monitoring or cross-border regulatory enforcement?

Communications is where we start because it's the highest-risk, highest-value problem. But the architecture is a general-purpose policy enforcement engine. Long-term vision: every policy, enforced on every action, by every human and AI agent, in real time. If you can enforce SEC and FINRA in real time, you can enforce anything.