CIO&Leader in this candid conversation with Tarun Wig, Co-Founder and CEO of Innefu Labs, outlines what it takes to turn AI initiatives into mission-ready deployments, why explainability and resilience are non-negotiable, and how his team is preparing for a GenAI-powered future with ProphecyGPT.
CIO&Leader: How do you define success when transitioning an AI initiative from pilot to production?
Tarun Wig: Success in transitioning AI from pilot to production is defined by three key metrics: sustainable business impact, operational resilience, and seamless user adoption. At Innefu Labs, we measure success not just by technical performance, but by how effectively the AI product handles real-world complexities while delivering consistent ROI. A successful transition means our AI systems maintain accuracy under varied conditions, process enterprise-scale volumes, and integrate smoothly with existing workflows. Most importantly, success is achieved when endusers embrace the product as it transforms their daily operations rather than creating additional friction.
CIO&Leader: What are the core pillars of your current enterprise AI strategy?
Tarun Wig: Our enterprise AI strategy is built on four fundamental pillars that reflect our Cybersecurity focus. First is our security-first AI architecture, ensuring AI solutions enhance security posture while maintaining the highest data protection standards. Second is domain-specific intelligence, where we develop specialized AI capabilities for Cybersecurity, Intelligence Fusion Centre (IFC), Digital Forensics, and Predictive Intelligence rather than pursuing generic solutions. Third is hybrid human-AI collaboration, designing systems that augment human decision-making in security contexts where human judgment remains irreplaceable. Finally, we design for interoperability and scalability, enabling seamless deployment across cloud, on-premises and air-gapped systems.
CIO&Leader: What key AI use cases have successfully moved into production, and what measurable impact have they delivered?
Tarun Wig: At Innefu Labs, several AI use cases have successfully transitioned from pilot to production, delivering substantial measurable impact for our clients. In Policing, Predictive Policing models have significantly improved force deployment planning and operational efficiency. In Defence, AI-powered Intelligence Fusion Centre has enhanced situational awareness in high-stake environments. On the Cybersecurity front, platforms like RapiDFIR and AuthShield have enabled proactive threat detection and secured privileged access using behavioral analytics. Across all deployments, the outcome has been faster response, improved risk visibility, and stronger operational control.
CIO&Leader: What infrastructure or architectural changes were necessary to scale AI effectively within your organization?
Tarun Wig: To scale AI effectively, we focused on building a flexible, resilient architecture that supports rapid deployment and seamless integration. This involved enhancing computing capabilities, ensuring data flow efficiency, and prioritizing security and compliance, allowing AI systems to perform reliably across diverse operational environments.
CIO&Leader: What are the biggest challenges you’ve faced in operationalizing AI, and how have you addressed them?
Tarun Wig: Operationalizing AI at scale requires aligning technology, data, and mission-specific outcomes. One of the ongoing challenges has been ensuring that models remain explainable, secure, and adaptable across highly sensitive and diverse environments. To address this, we’ve adopted a modular approach to model deployment, reinforced with continuous validation loops, synthetic testing environments, and human oversight. Our focus has always been on delivering AI that is not only accurate, but also trusted and accountable, especially in sectors where decisions carry significant impact.
CIO&Leader: How are you preparing your workforce for scaled AI adoption, and what organizational shifts have been required?
Tarun Wig: We’ve invested heavily and built structured training programs focused on AI, machine learning, and their application in cybersecurity. Our teams are cross-functional, combining data scientists, engineers, and domain experts to work together on real-world problems. We also encourage an AI-first mindset across the company. Product teams are trained to think about automation and intelligence from the start, making AI an essential part of every product we design.
CIO&Leader: Looking ahead, what does your AI roadmap over the next 12–18 months look like — especially in terms of GenAI or foundation model deployments?
Tarun Wig: Our GenAI roadmap over the next 12–18 months is centred around scaled deployment of ProphecyGPT,the world’s first offline, on-premises LLM built specifically for Defence and Intelligence. This secure platform enables natural language querying of complex datasets across formats and languages, streamlining reporting and accelerating high-stakes decision-making. In parallel, we’re advancing the convergence of AI and mechatronics, bringing together intelligent software and adaptive hardware to develop autonomous, responsive systems for Defence and Internal Security applications.