Authored by Jaydeep Singh, General Manager for India, Kaspersky
The numbers coming out of India's security community tell an optimistic story. A 2026 survey of over 100 Indian IT security decision-makers found that 54% of organizations plan to build a Security Operations Center within the next twelve months. Every single respondent (100%) plans to incorporate AI into their security operations.
That kind of unanimity is rare in enterprise technology. It is also, on its own, insufficient. In cybersecurity, the belief that you are protected when you are not is more dangerous than knowing you are exposed. And right now, across India's security landscape, ambition is running well ahead of the operational foundations required to make it real.
The execution gap no one is talking about
The same survey that captures this enthusiasm also maps its fault lines. The most critical barrier, cited by 47% of respondents, is a lack of high-quality training data, the foundational input without which AI models produce outputs that are unreliable at best and actively misleading at worst. A shortage of qualified AI expertise within security teams is a concern for 37%. The cost of developing and sustaining AI-driven solutions is 30%. Nearly as many flag a shortage of suitable solutions in the market, and 26% have begun encountering new threat vectors introduced by AI usage itself.
What security teams want AI to deliver is, broadly, the right set of expectations, stronger threat detection through automated anomaly analysis, better accuracy through machine learning, smarter correlation of incidents across complex environments. The problem is not the destination. It is the distance between where most teams stand today and what responsible deployment actually requires.
AI amplifies what is already there
AI does not fix a weak SOC. It scales whatever is already there, capability and dysfunction alike. Deployed into an environment with poor data hygiene, it automates poor decisions at volume. Without clear escalation workflows, it produces alert noise that exhausts analysts rather than directing them. The foundation determines the outcome, and if that foundation is underdeveloped, AI is not an accelerant, it is a multiplier of the underlying problem.
The organisations that have genuinely matured their AI-driven security operations share one distinguishing trait: it was never the sophistication of the tools. It was the discipline of the groundwork: clean data pipelines, defined workflows, analysts trained to act on model output rather than simply receive it. The fastest adopters were rarely the most effective ones.
Mythos, the frontier AI model that autonomously uncovered vulnerabilities hidden for years inside widely used systems, has sharpened this argument. What a post-Mythos market rewards is not access to the most capable model, but the proprietary telemetry, operational context, and human judgment that no general-purpose AI can replicate.
As frontier models make vulnerability discovery faster and cheaper, the defensive premium shifts to the layer only experience builds: real deployment history, supply-chain intelligence, threat-actor attribution, and analysts who know which outputs to trust.
For SOC teams building in India today, the implication is direct. The durable advantage is not in adopting the newest capability first, it is in having the data depth and analyst skill to make any capability count. Mythos does not change what good security operations require. It raises the cost of not having them.
What maturity actually looks like
SOC maturity is not a function of which tools are deployed. It is a function of whether an organisation can reliably detect, investigate, and respond to a threat within an acceptable window, with or without AI in the loop.
That requires documented playbooks, not just purchased licenses. Analysts who know how to triage an alert, not just acknowledge one. Leadership that can define what good looks like in operational terms, mean time to detect, mean time to respond, false positive rates rather than measuring success by adoption milestones alone. And data governance is robust enough to produce inputs that an AI model can actually reason over meaningfully.
For organisations in the planning phase, the sequencing question is the most consequential one. The instinct to deploy AI first and build processes around it is understandable, particularly when the threat environment is moving fast. But it tends to produce the worst of both worlds: AI that underperforms because the foundation isn't ready, and teams that over-trust outputs they haven't yet learned to interrogate.
The Real Competitive Advantage
As AI becomes table stakes across enterprise security, the differentiator will no longer be access to the technology, it will be the organisational discipline to deploy it well. That means treating data quality as a strategic asset, analyst capability as a long-term investment, and process maturity as the prerequisite rather than the afterthought.
India's SOC landscape is at a genuinely consequential moment. The decisions security leaders make in the next twelve to eighteen months about sequencing, about foundations, about what success actually means will define the effectiveness of their security operations for years beyond that. The ambition is clearly there. The question now is whether the structural work will follow.
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