In an exclusive conversation with IT Voice, Mr. Girish Hirde, Global Delivery Head at InfoVision, shares how AI is transforming the global cybersecurity landscape and reshaping cloud protection strategies. He discusses the evolution of Zero Trust frameworks, proactive threat detection, and how InfoVision’s AI-powered platforms like.
IT Voice- How do you see AI transforming the global cybersecurity landscape, particularly in cloud environments?
Girish Hirde- I believe AI will fundamentally rewire how we secure cloud environments by converting static defenses into continuously learning, adaptive systems that scale with modern architectures. AI brings the ability to ingest vast telemetry from multi cloud and hybrid estates, correlate events across containers, serverless functions and edge nodes, and predict compromise scenarios before they escalate, enabling predictive threat intelligence and automated containment at machine speed.
At InfoVision, we are advancing this paradigm by integrating our JARVIS AI platform with a Zero Trust foundation so identity, behavior and context sit at the core of cloud access decisions. We further extend this strategy by leveraging Invisinet’s cloaking capabilities and first-packet authentication at the cloud edge, minimizing exposed attack surface and securing hybrid IT-OT environments without slowing innovation. The net result is dramatically reduced dwell time, fewer false positives, and security that flexes with development velocity rather than impeding it, allowing business teams to innovate while maintaining resilient safeguards.
IT Voice- In your opinion, what are the most pressing cybersecurity challenges enterprises face in today’s AI-driven world?
Girish Hirde- The most urgent challenges today stem from AI becoming both an accelerator and a threat vector. Adversaries are now weaponizing AI for automated reconnaissance, deep-fake social engineering, model and data poisoning and scalable, precision-driven attacks. At the same time, enterprises struggle with fragmented observability across multi-cloud, edge and SaaS ecosystems, while privacy, model governance and regulatory expectations add new layers of complexity. The talent gap compounds these risks, making it harder to operate security programs at the speed of modern environments. The path forward requires robust data governance, continuous validation of AI models, and adaptive automation and continuous workforce upskilling to stay ahead of evolving AI-driven threats. .
IT Voice- How is the Zero Trust framework evolving in the context of increasing AI adoption across businesses?
Girish Hirde- Zero Trust is shifting from a static control model to a living, adaptive security architecture where identity and continuous verification underpin every action across the digital estate. As AI accelerates the volume, velocity and autonomy of interactions across cloud, OT and edge systems, Zero Trust must operate at machine speed and at the very first packet of communication. At InfoVision, we bring this evolution to life by integrating capabilities like Invisinet’s secure cloaking, identity-based micro segmentation and first-packet authentication, ensuring that networks and industrial assets remain invisible to unauthorized actors and every user and device is verified before a single instruction executes.
Dynamic policy enforcement and continuous device posture validation support hybrid IT and IIoT growth, while protecting air-gapped environments and AI-enabled industrial systems. This model transforms Zero Trust from a security framework into a digital trust engine that enables automation, operational continuity and safe AI innovation across critical infrastructure and enterprise environments.
IT Voice- Could you elaborate on how AI-powered cloud security differs from traditional cloud protection models?
Girish Hirde- AI-powered cloud security represents a fundamental shift from perimeter-centric, signature-based controls to adaptive, behavior-driven defense operating continuously and at cloud-native speed. Traditional models depend on static rules and periodic scans, which cannot keep pace with highly dynamic, containerized, microservices-based and serverless environments. AI analyzes process behavior, API activity and east-west traffic patterns in real time to surface zero-day, fileless and lateral-movement threats that legacy tools routinely miss.
Deep telemetry, automated remediation and intelligent policy orchestration ensure security scales with application velocity and infrastructure elasticity. Cloaking and identity-first access mechanisms at sensitive entry points enhance trust while preserving frictionless user and workload access. The result is AI-powered cloud security transforms defense from reactive to predictive, enabling enterprises to anticipate threats, enforce dynamic policies, and scale protection in lockstep with business innovation without compromising user experience and compliance.
IT Voice- What innovations or best practices are helping organizations stay ahead of evolving cloud threats?
Girish Hirde- Organizations that stay ahead are embracing secure by design practices, integrating DevSecOps from build to runtime, applying continuous threat modeling and chaos engineering to surface gaps, and using AI to prioritize fixes based on real world risk. Strong telemetry and data engineering to feed ML models, coupled with model governance and feedback loops, reduce false positives and accelerate detection. Micro segmentation and identity first architectures limit blast radius and make lateral movement economically infeasible for attackers. Sharing anonymized threat intelligence across industry consortia and automating compliance workflows with intelligent GRC models also help organizations move from reactive patching to strategic prevention.
IT Voice- How is AI enabling proactive threat detection and incident response rather than reactive defense mechanisms?
Girish Hirde- AI enables proactive detection and incident response by turning massive telemetry into early indicators of compromise and by orchestrating validated containment actions automatically. Machine learning profiles normal behavior and surfaces deviations that would be invisible to human analysts at scale, while playbook automation can isolate endpoints, reconfigure network micro-segments, or revoke risky sessions in seconds. Building on the shift toward adaptive, behavior-driven defense models and zero-trust principles, AI-augmented SOC workflows and risk-based automation ensure responses are prioritized, measurable, and executed at machine speed.
IT Voice- What role does data analytics and machine learning play in enhancing real-time threat visibility?
Girish Hirde- Data analytics and machine learning are the backbone of real time threat visibility because they convert noisy event streams into correlated insights and actionable signals. By aggregating telemetry from endpoints, network flows, cloud APIs and OT gateways, ML models can identify subtle reactive firefighting patterns indicative of supply chain intrusions, credential misuse. Effective models depend on high quality feature engineering, continuous retraining, and feedback loops from human analysts to reduce concept drift and false positives. In our practice we feed supervised and unsupervised models with curated telemetry through JARVIS and use the outputs to enrich SOC workflows and automated containment. The combination of broad telemetry, smart analytics and model governance yields situational awareness that surfaces threats in minutes instead of days.
IT Voice- How does InfoVision integrate AI and automation into its cybersecurity services and client engagements?
Girish Hirde- We integrate AI and automation into cybersecurity engagements through end to end platforms and pragmatic services that marry detection, response, and governance. Our JARVIS AI platform automates data engineering, model development and operational monitoring while our REG model prioritizes risks in business terms so remediation focuses on what matters most. We operate AI augmented MDR and Next Gen SOC offerings that include automated triage, playbook execution and continuous learning, and we embed Invisinet to enforce Zero Trust at the network level, including first packet authentication and micro segmentation for IT and OT. Client programs begin with discussion with business problem to solve, pilot with defined KPIs, ROI, and knowledge transfer to customer team so teams are enabled to run AI powered security sustainably rather than outsourcing all expertise.
IT Voice- Can you share a few success stories or key milestones that reflect InfoVision’s impact in strengthening enterprise cybersecurity?
Girish Hirde- Our impact is best illustrated by measurable outcomes where technology and process converged to reduce risk and friction. We helped a Fortune 500 client automate seventy percent of compliance reporting through our REG model, cutting audit timelines by nearly half, and we deployed AI driven detection and containment for a healthcare customer that prevented a major operational outage by isolating compromised devices before ransomware could spread. On the industrial side Invisinet’s InvisiGate gateway to gateway model has protected complex IIoT environments, leveraging our portfolio of patents and military grade innovation to cloak systems and enforce identity-based access. These milestones show how combining AI, automation and Invisinet’s Zero Trust capabilities delivers tangible resilience and business continuity.
IT Voice- What are InfoVision’s upcoming strategic priorities in the cybersecurity and risk management space?
Girish Hirde- Our strategic priorities center on scaling AI-driven managed security services and accelerating Zero Trust adoption across both IT and OT environments. We’re investing heavily in advanced model governance and explainability to ensure AI decisions remain transparent, auditable, and trusted. Another key focus is expanding regulatory assurance offerings in high-growth markets and deploying Invisinet into critical infrastructure sectors where risk and impact are highest. To keep our innovation pipeline strong, we’re strengthening partnerships with leading cloud providers and academic research labs. Finally, we aim to democratize enterprise-grade security by making global-class MDR and SOC capabilities accessible to mid-market customers—without the complexity that typically comes with such programs.
IT Voice- As AI continues to advance, how do you foresee the balance between innovation and risk shaping the future of enterprise security?
Girish Hirde- As AI advances the balance between innovation and risk will be defined by governance, transparency and the ability to test systems under adversarial conditions. Innovation will keep delivering productivity and defensive advantages but without rigorous model risk management, continuous validation and clear accountability the systems we build could introduce new attack vectors. I expect frameworks that combine explainability, provenance and secure training data pipelines to become mainstream, and regulatory regimes will demand evidence of controls. Technologies like Invisinet that enforce identity and minimize exposure at the network layer will act as critical safeguards, allowing organizations to experiment with AI while preserving operational safety and meeting compliance obligations.
IT Voice- What advice would you give to CIOs and CISOs preparing for the next wave of AI-driven cyber threats?
Girish Hirde- My advice to CIOs and CISOs should treat AI as both an opportunity and a risk. Invest in upskilling teams, embed security into DevOps workflows, and automate repetitive tasks to free talent for strategic investigations. Start with high-value pilots that prove ROI and partner with trusted vendors to scale AI-driven security without overwhelming internal teams. The ultimate goal is to move from reactive defense to a proactive posture that strengthens resilience and supports business innovation.
