Kunal Purohit, President – Next Gen Services at Tech Mahindra, tells us how the convergence of AI-driven network security and Big Data analytics is revolutionising the way businesses operate, from enhancing security to enabling smarter, data-driven decisions.
Kunal Purohit, President – Next Gen Services at Tech Mahindra, tells us how AI-driven innovations are shaping the convergence of network security platforms and Big Data analytics, enabling organisations to adapt to dynamic digital environments with greater agility and intelligence.
The integration of AI, Generative AI and Machine Learning into network and security platforms is not only streamlining operations but also enhancing the ability to predict, detect and mitigate cyberthreats in real-time.
Meanwhile, in the realm of Big Data, AI-powered analytics is empowering businesses to transform raw data into actionable insights that drive smarter decisions. This convergence of network security and Big Data is facilitating a new era of operational efficiency, predictive capabilities and proactive risk management.
Network and security platforms
What are the top trends and innovations taking place in the convergence of network and security platforms?
We are seeing a shift from siloed infrastructures to AI-first, unified network and security fabrics. Cloud-native, self-healing and intent-based networks are becoming the norm, with orchestration and real-time security integrated at the core.
Generative AI is automating operations, interpreting intent, and simulating network changes before deployment. Traditional firewalls are evolving into adaptive, AI-enhanced systems. Continuous security validation is replacing static models, with autonomous testing detecting vulnerabilities in real-time. Cloud-distributed 5G Core architectures and AI-driven security orchestration are reshaping how infrastructure supports business needs. The future lies in intelligent, converged systems that continuously learn, adapt, and protect across dynamic digital environments.
What are the pain points, that CIOs and IT administrators have in the convergence of network and security platforms?
CIOs and IT leaders face several hurdles, including integration of legacy systems with modern platforms and managing multi-vendor environments, both leading to interoperability gaps and security inconsistencies. Compliance with evolving regulations like General Data Protection Regulation (GDPR) adds further complexity. A shortage of skilled professionals and escalating costs for infrastructure overhaul remain major concerns. Integrating security into performance-sensitive networks can create latency and scalability issues and ensuring consistent security policies across hybrid environments is another challenge. Managing AI-driven systems also requires continuous fine-tuning. Additionally, vendor lock-in and challenges in incident response co-ordination must be addressed. At Tech Mahindra, we deliver scalable, vendor-agnostic solutions that maintain performance, ensure compliance and simplify convergence across hybrid environments.
How is AI, Generative AI helping to reduce the converged workload for NOC and SOC administrators?
AI and Generative AI are transforming Network Operations Center (NOC) and Security Operations Center (SOC) operations by automating alert triage, incident response and anomaly detection, freeing up teams to focus on complex tasks. Predictive analytics pre-empt network disruptions, while AI rapidly identifies emerging threats based on behavioural anomalies. Generative AI can suggest and auto-generate resolution steps, significantly reducing response time. At Tech Mahindra, we leverage these capabilities to enhance service uptime and operational efficiency, Tech Mahindra’s Ops amplifAIer platform helps service engineers by creating a unified view, root cause analysis, remediation with code/script generation. By prioritising incidents by risk, minimising false positives and offering real-time insights, AI reduces workload and also elevates the accuracy and speed of decision-making across security and network operations
What is the required skill sets to manage the convergence of network and security platforms?
Professionals need a hybrid skill set blending AI literacy, cybersecurity expertise and cloud-native infrastructure design. Key competencies include understanding ML models, responsible AI practices and securing AI-generated code. Designing AI-augmented, edge-enabled architectures is now essential. With automation reducing manual coding, the focus shifts to validating AI outputs, ensuring explainability and aligning systems with business objectives. Soft skills like adaptability, interdisciplinary collaboration and creative problem-solving are equally critical. At Tech Mahindra, we foster continuous learning and emphasise ethical engineering to ensure systems remain transparent, secure and business-aligned. Success will favour those who see AI as a collaborator, not just a tool.
Big Data and predictive analytics
What are the top trends and innovations taking place in Big Data and predictive analytics?
Big Data and predictive analytics are evolving rapidly with AI and Machine Learning driving real-time insights. Synthetic data is emerging as a key innovation, enabling model training without privacy risks or high acquisition costs. Edge analytics is gaining momentum, reducing latency by processing data closer to the source. Cloud-based platforms are democratising access to advanced analytics, offering scalable and cost-effective solutions. Data democratisation empowers more users to derive value from data, enhancing agility. Together, these trends are reshaping industries by improving forecasting, accelerating innovation, and delivering smarter, faster business decisions with broader organisational involvement in analytics.
What are the business benefits from implementing Big Data and predictive analytics platforms?
Big Data and predictive analytics enable smarter, faster decisions that drive business success. They replace gut instinct with real-time insights, enhancing forecasting, risk detection and operational efficiency. Organisations can optimise costs by identifying inefficiencies and improving resource use. Personalisation at scale strengthens customer loyalty and experience. Predictive models also support proactive adaptation to market shifts. These platforms uncover untapped revenue opportunities and help craft competitive pricing strategies. Beyond numbers, they foster a culture of innovation and continuous improvement. For modern enterprises, adopting Big Data and analytics is a strategic imperative—transforming decision-making from reactive to anticipatory, and unlocking long-term value.
What are the technology challenges around implementation and management of Big Data and predictive analytics platforms?
Organisations face multiple technology challenges when implementing Big Data platforms. Integrating data from diverse sources while ensuring consistency and quality is a major hurdle. As data volumes grow, scalability becomes essential without compromising performance or cost. Security and privacy compliance—especially with regulations like GDPR—remain on-going concerns. A shortage of skilled professionals complicates real-time analytics implementation. Infrastructure costs and the need for continuous optimisation further strain resources. Model drift is a key challenge, maintaining, monitoring and validating becomes critical for predictive models, Tech Mahindra’s VerifAI solution is built to address this challenge. Lastly, organisational change management is often overlooked but crucial, as successful adoption demands shifting traditional mindsets toward data-driven operations. Addressing these challenges requires a blend of technical excellence, strategic vision and cross-functional collaboration.
What is the required enterprise skill sets around implementation and management of Big Data and predictive analytics platforms?
Enterprises need a multi-disciplinary skill set to implement and manage Big Data platforms effectively. Data engineering and cloud architecture skills are vital for building scalable pipelines using tools like Hadoop, Spark and cloud services. Data scientists must be adept in Machine Learning, modelling and analytical tools. Data governance experts must ensure compliance with regulations such as GDPR and Central Consumer Protection Authority (CCPA). Business Intelligence tool proficiency is essential to convert insights into actions. Soft skills like collaboration, project management and change leadership help align cross-functional teams. At Tech Mahindra, our Centres of Excellence drive continuous upskilling and foster innovation across enterprise-wide data initiatives.
