Cyberattacks on Indian companies continue to be on the rise. According to CERT-In, eight out of ten local companies were affected by data theft, espionage, or sabotage in 2024. These cyberattacks in India have led to financial losses in recent years, with the country ranking among the top globally for its vulnerability to ransomware and phishing attacks.
In addition, security vulnerabilities are expected to worsen with the growing sophistication and scale of AI-enhanced cyberattacks. Cloudflare’s State of Application Security 2024 report highlights how the rise of generative AI introduces new risks, such as exploitable APIs in AI models and the deployment of flawed, AI-generated code. These challenges are aggravated by increasing DDoS attacks, more efficient botnets, and rising supply chain risks linked to third-party scripts. As developers face pressure to rapidly deliver AI-driven features, the attack surface continues to expand, magnifying the threat landscape.
Security teams recognize this issue, as indicated in Cloudflare’s report “Navigating the New Security Landscape: Asia Pacific Cybersecurity Readiness Survey,” which states that 87% of cybersecurity leaders are concerned about AI increasing the sophistication and severity of data breaches. Exploited, unprotected apps can lead to business disruptions, financial losses, and critical infrastructure collapses.
As global organizations embrace the AI boom, traditional security measures are no longer enough to safeguard today’s dynamic IT environment. To stay ahead of ever-evolving threats, modern security systems must be agile, intelligent, and continuously adaptive.
Three seismic shifts are converging to disrupt current cloud security strategies: the industrialization of AI-powered development, the “democratization” of sophisticated attacks, and the dissolution of traditional security boundaries. Together, these developments are reshaping the IT security landscape in India.
The industrialization of AI goes far beyond AI-generated code. Developers are experimenting with agent-based, fully autonomous systems that build and modify cloud-based applications progressively with minimal human oversight. This marks a transition from human-paced development to machine-speed innovation, which dramatically increases the speed and scale of application deployment. As a result, attack-prone areas are expanding faster than traditional security tools can detect, let alone defend.
Meanwhile, the threat landscape is evolving just as rapidly. AI is “democratizing” complex attack capabilities that were previously limited to state actors. Autonomous malware now adapts in real time and learns from defenses to evade them. These are not just faster attacks—they now operate beyond human responsiveness and make decisions at machine speed.
In India, the rise of digital payments, smart cities, and AI-driven business models means security must evolve alongside innovation.
Lastly, the concept of a fixed security perimeter is obsolete. Hybrid work environments, a surge in connected devices, and AI-driven multi-cloud infrastructures have dissolved the boundaries between internal and external networks. Data and applications now exist everywhere, they are constantly in motion and accessible from virtually anywhere.
So, how do companies go from here? Incremental improvements to existing cybersecurity models are no longer sufficient. Rather, security teams must adopt new security architecture built with AI-driven challenges and decentralized data postures in mind.
Edge-enforced Zero Trust for a digital-first India: For India, where businesses operate across multiple regions and rely heavily on cloud and hybrid infrastructure, implementing zero-trust security at the edge will be crucial for long-term resilience. The modern threat landscape requires Zero Trust architecture deployed at the edge that authenticates continuously, evaluates risk in real time, and adapts dynamically to user behavior and context. Services like Cloudflare's Zero Trust Network Access (ZTNA) and Cloudflare One platform move enforcement from centralized gateways to a global edge network.
AI-native security measures: The rise of AI-powered threats requires security that is not only AI-assisted but AI-native. This means designing and deploying security measures that learn, evolve, and act in real time without the need for human input. Instead of relying on analysts to manually define and adjust policies, AI-native systems continuously observe application behavior and autonomously create and refine safeguards.
Streamlined security systems to combat fragmented security: Fragmented security stacks hinder response times, create blind spots, and increase operational burden. With AI accelerating both development and attacks, organizations must consolidate their tools and adopt unified platforms that provide end-to-end visibility and correlation. By centralizing data and control, it enables faster decision-making, real-time threat detection, and seamless orchestration across the technology stack—helping teams stay ahead of AI-driven threats
Prioritize cybersecurity awareness training: With the adoption of AI in business operations, it is crucial to educate employees on cybersecurity risks. Cybersecurity training should go beyond the basics, covering topics like recognizing phishing emails, avoiding malware, and understanding data protection protocols. Additionally, conducting simulated cyber-attacks as part of the training can give employees practical experience in identifying threats.
In the next 18 months, a clear divide will emerge between organizations that proactively modernize their security for the AI-driven future and those that fall dangerously behind and become vulnerable. Autonomous systems are already deploying applications with minimal human oversight, while attacks are becoming more sophisticated, adapting and evolving faster than traditional systems can detect. Traditional security approaches designed for predictable threats and human response times are not only outdated—they are becoming liabilities.
For Indian enterprises, where digital adoption is at an all-time high, securing AI-driven systems today is crucial to ensure long-term business resilience. The future will not be about building better walls but about developing intelligent, adaptive ecosystems that evolve at the speed of innovation.
[Disclaimer: The views expressed are solely of the author and Analytics Insight does not necessarily subscribe to it. Analytics Insight shall not be responsible for any damage caused to any person/organization directly or indirectly.]