Introduction
Artificial Intelligence has crossed a decisive threshold in cyber security. No longer confined to experimental analytics or niche automation, AI is rapidly becoming the strategic backbone of modern cyber defense. In India, this transition is unfolding against the backdrop of an expanding digital economy, aggressive digitization of public and private services, and a threat landscape increasingly shaped by intelligent, adaptive adversaries.
The State of AI Adoption for Cyber Security in India report, released in December 2025, provides one of the most detailed assessments yet of how Indian organizations are navigating this inflection point. Drawing insights from more than 160 organizations across sectors such as technology, BFSI, government, healthcare, and critical infrastructure, the study reveals a paradoxical reality. India shows strong ambition and intent to embed AI into cyber security operations, yet operational maturity remains uneven and constrained by cost, skills, governance gaps, and data readiness challenges.
What emerges is a picture of a nation at a strategic crossroads. AI is simultaneously strengthening defences and empowering attackers. Organizations are being forced to rethink not only tools and technologies, but also talent models, governance frameworks, risk appetite, and board-level accountability.
Background and Context
India’s digital surface has expanded dramatically over the last decade. Cloud adoption, fintech platforms, digital identity systems, connected supply chains, and API-driven ecosystems have created unprecedented scale and complexity. At the same time, threat actors have evolved beyond manual exploitation into automated, AI-enabled attack models capable of operating at machine speed.
The report underscores that traditional signature-based and perimeter-focused security models are no longer sufficient. AI has emerged as the only viable mechanism to detect anomalies across massive datasets, correlate multi-vector threats, and respond within shrinking time windows.
This urgency is further amplified by India’s national digital ambitions. As the country moves toward a trillion-dollar digital economy, cyber security is no longer a back-office function. It has become a foundational enabler of trust, resilience, and economic continuity. AI-driven cyber security is now viewed as a strategic necessity rather than an optional enhancement.
Current State of AI Adoption
One of the most striking findings of the report is the gap between intent and execution.
Approximately 79 percent of Indian organizations surveyed plan to integrate AI or machine learning into their cyber security or operational tools. However, only around 8 percent report that AI has been fully integrated into production environments across their security stack. The majority remain in pilot, proof-of-concept, or limited deployment stages.
Nearly 40 percent of organizations are still experimenting with AI in controlled environments, validating performance, scalability, and return on investment before committing to enterprise-wide rollouts. Another 27 percent have begun phased deployments in select security functions, reflecting cautious but deliberate progression.
This slow transition from experimentation to scale is not driven by lack of belief in AI’s value. Instead, it reflects structural constraints, ranging from infrastructure costs and talent shortages to governance uncertainty and integration challenges with legacy systems.
Why Organizations Are Turning to AI in Cyber Security?
The motivations behind AI adoption are both operational and strategic.
The single most cited objective is the reduction of Mean Time to Detect (MTTD) and Mean Time to Respond (MTTR). About 25 percent of organizations identify faster detection and response as their primary driver. With alert volumes exploding and adversaries using automation, manual triage has become unsustainable. Predictive security analytics and proactive risk scoring follow closely, cited by over 23 percent of respondents. Organizations are seeking anticipatory capabilities that can identify weak signals, forecast attack paths, and surface risks before exploitation occurs.
Other key motivations include continuous compliance monitoring, anomaly detection in regulated environments, reduction of false positives, and automation of Tier 1 and Tier 2 SOC tasks through SOAR integration. Collectively, these drivers indicate a shift from reactive defense toward intelligence-led, predictive security models.
Sectoral Differences
AI adoption patterns vary significantly across industries. Technology and IT services firms prioritize continuous compliance monitoring and anomaly detection, reflecting their exposure to global privacy regulations and customer-facing service-level commitments. For these organizations, audit readiness and trust assurance are competitive differentiators.
In contrast, banking and financial services organizations focus heavily on automating incident response and fraud detection. The need for rapid containment of financially motivated attacks and regulatory scrutiny drives aggressive investment in AI-enabled SOC automation. Government, education, and non-profit organizations face a different set of constraints. Budget limitations, legacy infrastructure, and data sovereignty requirements slow adoption, even as these sectors recognize the growing threat from AI-enabled attacks.
Economic value creators such as manufacturing, energy, healthcare, and telecom must address operational technology convergence and supply-chain risks, making AI adoption both complex and mission-critical.
Investment Trajectory in AI
The report highlights a clear evolution in how Indian organizations are funding AI-driven cyber security. Nearly 64 percent of respondents indicate that AI investments are now aligned with multi-year risk management roadmaps rather than reactive spending triggered by incidents or regulatory mandates. This marks a significant maturation in strategic thinking. Technology and IT services firms lead in proactive investment, while BFSI organizations are rapidly formalizing AI-led security budgets to enhance resilience and maintain regulatory trust. Healthcare and government sectors remain comparatively nascent, constrained by funding and policy complexity.
Notably, 42 percent of CXOs believe that more than 10 percent of cyber security budgets should be allocated to AI-related capabilities, signalling growing board-level recognition of AI as a core security investment.
The Escalating AI Threat Landscape
AI’s dual-use nature is a central theme of the report. While defenders adopt AI to improve resilience, adversaries are leveraging the same technologies to scale and automate attacks. Approximately 31 percent of organizations identify AI-enabled supply-chain attacks, backdoored models, and coordinated multi-vector operations as the most dangerous emerging threats. Another 29 percent point to autonomous polymorphic malware and AI-driven zero-day discovery.
Deepfake-enabled social engineering, synthetic identity fraud, prompt injection attacks, and generative AI abuse are no longer theoretical risks. They are actively reshaping fraud, espionage, and intrusion campaigns across sectors.
Business Function Exposure
The report provides granular insight into which business functions are most vulnerable to AI-driven attacks. Finance and Human Resources teams experience the highest success rates of AI-enabled attacks. Their exposure to payments, approvals, payroll data, and identity systems makes them prime targets for deepfake impersonation, phishing, and MFA fatigue attacks.
Sales, marketing, and customer relations teams face moderate risk due to extensive external interactions, social platforms, and SaaS dependencies. In contrast, legal and IT security teams demonstrate lower attack success rates due to stronger governance enforcement and technical awareness. This uneven exposure underscores the need for function-specific AI defense strategies rather than uniform controls across the enterprise.
Key Barriers Slowing AI Security Maturity
Despite strong intent, organizations face persistent roadblocks. Financial overhead is the most cited barrier, affecting around 19 percent of organizations. High compute costs, cloud infrastructure demands, and ongoing model optimization strain budgets. Skills shortages follow closely at 17 percent, reflecting the scarcity of professionals who combine cyber security expertise with AI and machine learning proficiency.
Data quality and availability issues, unclear ROI measurement, regulatory ambiguity, and integration challenges with legacy systems further slow progress. Together, these barriers explain why many organizations remain stuck between pilots and production deployment.
CXO Perspectives
Executive leadership views AI in cyber security as both an opportunity and a risk. While 69 percent of CXOs express serious concern about AI-enabled cyber attacks, only 24 percent believe their organizations are fully prepared to handle them. About half rate their readiness as moderate, highlighting a significant confidence gap. Top executive concerns include sensitive data exposure to AI models, inconsistent or inaccurate outputs, model drift, prompt injection vulnerabilities, and lack of explainability in autonomous decisions. These concerns reinforce the demand for stronger AI governance and human oversight.
Operationalizing AI Governance
Governance emerges as a decisive factor separating experimentation from scale. More than 60 percent of organizations have moved from ad-hoc AI policies to structured governance frameworks. Over 52 percent now place AI accountability under board and CISO oversight, integrating AI risk into core leadership decisions. Human-in-the-loop controls remain critical. About 33 percent of organizations mandate human approval for AI-driven critical security actions, balancing automation with accountability and trust.
Deployment preferences also reflect governance priorities. While some organizations consume AI security capabilities via cloud APIs for speed and scalability, others prefer on-premises or private infrastructure to maintain sovereignty and control.
Outlook
Beyond defense, AI-driven cyber security is delivering measurable business value. Organizations report competitive advantages in predictive threat intelligence, faster response, operational efficiency, talent augmentation, customer trust, and supply-chain resilience. AI is increasingly viewed as an enabler of secure innovation rather than a cost centre. This reframing positions cyber security as an intelligence-driven capability engine, embedded into enterprise strategy rather than isolated within IT functions.
The findings of the report point to a clear conclusion. India is past the question of whether AI should be used in cyber security. The real challenge lies in how fast organizations can scale responsibly. The next phase will be defined by autonomous SOCs, human-AI hybrid defense teams, predictive risk prioritization, and governance-first architectures. Organizations that succeed will be those that align technology, talent, and trust under a unified strategy. As adversaries continue to weaponize AI, defensive capabilities must evolve at comparable speed. The ability to build secure, transparent, and resilient AI systems will determine not only organizational security posture, but also the credibility of India’s digital future.
Source:
State of AI Adoption for Cyber Security in India – December 2025, authored by the Data Security Council of India (DSCI) with support from Palo Alto Networks .
