As artificial intelligence shifts from experimental pilots to mission-critical decision-making across industries like finance, healthcare, insurance, and defence, the rules of enterprise compliance are being rewritten. Vivek Chandran, Founder of Risknox.ai, believes governance is the next indispensable layer, and the one that ensures AI systems are not just innovative, but also trustworthy, ethical, explainable, and auditable. In this conversation with Tech Achieve Media, Chandran outlines why traditional compliance models fall short for algorithmic systems, how governance can be embedded without stifling innovation, and the critical role real-time intelligence plays in reshaping risk management for sectors like cyber insurance.
TAM: With AI adoption soaring, why is governance becoming the next critical layer of enterprise compliance?
Vivek Chandran: AI is no longer confined to innovation labs, it’s now embedded in decision-making processes across all business units such as finance, healthcare, insurance, defence, and more. As these systems make increasingly consequential choices, ensuring they are trustworthy, ethical, explainable, and auditable becomes essential. Traditional compliance frameworks were not designed to evaluate these algorithmic behavior. Governance adds this missing layer by addressing ethical, legal, and operational risks specific to AI. Without proper governance, enterprises risk regulatory penalties, reputational damage, and unintended bias or harm, making governance not just a compliance obligation but a business imperative.
TAM: How can organizations operationalize AI governance frameworks without stalling innovation?
Vivek Chandran: The key is to embed governance within the AI lifecycle, and not around it. Organizations must move from checklist-based compliance to adaptive governance models that are agile, data-driven, and context-aware. At Risknox, we enable this by offering AI governance as a service, helping enterprises onboard AI projects into our platform, map them against global regulatory frameworks (like the EU AI Act or ISO 42001), and get continuous risk assessments and mitigation roadmaps. This approach allows innovation to scale, safely and transparently, without adding unnecessary friction to development pipelines.
TAM: In highly regulated sectors, what gaps do you see in current compliance models when it comes to algorithmic accountability?
Vivek Chandran: Current models often focus heavily on data privacy and information security but fall short in addressing model behavior, decision traceability, and post-deployment monitoring. For example, in banking or insurance, AI systems might automate credit scoring or underwriting, yet there’s minimal oversight into how decisions are made, if biases exist, or how models adapt over time. We see a clear need for tools that can quantify algorithmic risk, ensure explainability, and support audit trails. That’s a gap Risknox is actively addressing with our compliance engine and audit support modules for algorithmic systems.
TAM: What role does real-time threat intelligence play in reshaping underwriting and claims in the cyber-insurance domain?
Vivek Chandran: Real-time threat intelligence is transforming cyber insurance from a reactive instrument to a predictive and preventive one. By continuously analyzing threat data, ranging from vulnerabilities to attack vectors, insurers can build dynamic risk models that reflect the current security posture of an organization. At Risknox, our platforms ingest such intelligence and update cyber hygiene scores and predicted loss estimates accordingly. This allows for smarter underwriting, faster claims validation, and better alignment between risk exposure and policy premiums. It’s a shift from static questionnaires to living risk profiles.
TAM: What’s your outlook on regulatory trends, and are enterprises prepared for mandatory AI audits and risk disclosures?
Vivek Chandran: The regulatory landscape is moving fast, what was once advisory is quickly becoming mandatory. The EU AI Act is pioneering binding AI audits, and similar frameworks are emerging globally. In the next 12–24 months, we expect to see mandatory impact assessments, algorithm registries, and incident reporting obligations becoming standard. Unfortunately, most enterprises are still underprepared, with limited visibility into their AI inventory or risk exposure. That’s where solutions like Risknox come in. Our platform offers automated AI risk discovery, policy mapping, and continuous audit readiness to help businesses stay ahead of the curve.
TAM: What innovations are startups like Risknox bringing to streamline insurer-insured transparency in cyber risk postures?
Vivek Chandran: We’re bridging a critical trust gap between insurers and insureds through data-driven transparency. Traditional cyber insurance relies on manual assessments, vague risk scores, and self-declared security measures. Risknox changes that with a real-time cyber hygiene scoring engine, automated risk quantification using FAIR methodology, and a dynamic dashboard for insurers and enterprises alike. We also provide APIs to embed cyber risk scores directly into underwriting workflows. This not only accelerates policy issuance but ensures fairer pricing and better risk mitigation for all stakeholders.
