By: Pandiya Kumar Rajamony, EVP, Cognitive Infrastructure Services

In today’s digital landscape, organizations are increasingly adopting multi-cloud strategies to harness the flexibility, scalability, and cost-efficiency of diverse cloud platforms. However, this distributed architecture introduces significant challenges in data protection, management, and compliance. With data sprawled across public clouds like AWS, Azure, and Google Cloud, and on-premises infrastructure, traditional security perimeters become obsolete. This complex cloud architecture and other factors are driving the need for innovative data security.

The evolving data resilience landscape

The proliferation of applications across the clouds, adoption of AI and the emergence of quantum computing have significantly reshaped the data resiliency landscape. New regulations like NIS2 (EU) and Digital Operational Resilience Act (DORA) are enforcing stricter data protection and recovery standards.

With the rise of multi-cloud environments, a robust and unified approach is required to tackle the complexity of managing disparate security configurations, ensuring seamless data mobility, and maintaining compliance with regulations such as GDPR/DORA.

Enterprises are increasingly leveraging generative AI and advanced data analytics to extract deeper insights and uncover new revenue opportunities. However, these technologies also generate vast volumes of real-time data, which significantly increases operational complexity and heightens exposure to risks such as ransomware attacks and regulatory compliance violations.

Moreover, quantum computing threatens traditional encryption protocols, making legacy cryptographic methods vulnerable to future attacks. With AI assistance the frequency and sophistication of cyberattacks are increasing, driving the need for more robust data security.

These developments underscore the urgency for organizations to rethink their data protection strategies. The convergence of AI, quantum computing, and multi-cloud architectures demands a proactive, resilient, unified and adaptive approach to data resiliency—one that not only addresses current threats but also anticipates future vulnerabilities.

In response, enterprises are shifting towards quantum-resistant cryptography and AI-powered threat detection systems. For instance, financial institutions are leveraging AI to monitor user behavior and detect fraudulent activities in real time. Similarly, organizations are adopting lattice-based cryptography and quantum-safe TLS protocols to safeguard sensitive data against quantum-capable adversaries.

In this increasingly complex and evolving environment, the zero trust framework emerges as a critical foundation for cybersecurity and ensuring end-to-end data security and resilience across multi-cloud operations. Data resiliency has become an integral part of cybersecurity and zero trust principles are being applied on data resiliency. Governments and regulatory bodies are increasingly advocating for zero trust principles to enhance data resiliency.

Key pillars of a zero trust approach

To achieve robust data resiliency across multi-cloud environments, organizations must adopt a multi-pronged strategy rooted in the zero trust security framework. This approach mandates verification of every access request, regardless of origin, and incorporates several key components:

1. AI-led frontline defence
   Artificial intelligence plays a pivotal role in enhancing data resiliency. AI-powered threat detection systems can analyze vast volumes of security logs to identify anomalies and suspicious activities in real time. For instance, financial institutions use AI to detect fraudulent transactions by monitoring user behavior patterns.AI also enables security automation, allowing organizations to swiftly isolate compromised systems and block malicious IP addresses during an attack. This reduces response time and limits operational disruption. Furthermore, AI-based security posture management tools continuously assess cloud environments for misconfigurations and recommend corrective actions, such as updating access controls or patching vulnerabilities.By integrating AI into their zero trust data resiliency strategy, organizations can proactively identify threats, automate responses, and maintain a resilient security posture of data across complex multi-cloud infrastructures.

2. Zero trust practices in data resiliency
   • Micro-segmentation and least privilege access

   By dividing networks and storage into isolated zones, we minimize the blast radius of any breach. Granting users only the access they truly need—nothing more—reduces the risk of unauthorized data changes. Use AI-based behavioral analytics to dynamically adjust access levels based on user activity patterns.
   Example: A marketing analyst accesses client data, while an IT administrator has broader system privileges.

   • Multi-factor authentication (MFA)
     MFA adds a critical layer of defense by requiring multiple forms of verification, making it harder for attackers to gain access even if passwords are compromised. Leverage AI to detect anomalies in login behavior and trigger adaptive MFA challenges.
     Example: Employees accessing cloud storage must use both passwords and authenticator tokens.
   • Continuous monitoring and data encryption
     Real-time tracking of network traffic and system events helps spot threats early. Encrypting data both in transit and at rest ensures that even intercepted data remains unreadable. Deploy AI-powered monitoring tools that learn from historical data to flag unusual activity instantly.
     Example: AI models can detect subtle deviations in traffic patterns that signal a breach attempt.
   • Cyber resilience measures
     Tools like endpoint detection and response (EDR), security information and event management (SIEM), and intrusion detection/prevention systems provide full-spectrum threat visibility. Incident response planning and vulnerability management further reinforce defenses. Use AI to automate threat detection, priorities alerts, and orchestrate rapid incident response.
     Example: AI-driven SIEM systems can correlate events across endpoints to identify coordinated attacks.

3. Data infrastructure modernization with resilient architecture
   Implementing synchronous and asynchronous replication between on-premises and cloud storage ensures business continuity. Immutable storage, such as write once, read many (WORM), protects against ransomware and accidental deletions. Automated storage tiering optimizes performance and cost by dynamically shifting data across hot, warm, and cold tiers.

4. Quantum-safe cryptography
   To counter emerging quantum threats, organizations should adopt quantum-resistant encryption techniques such as lattice-based cryptography and quantum-safe TLS protocols. Quantum key distribution (QKD) and secure cryptographic hashing further enhance data protection.By combining zero trust principles with AI-driven automation and quantum-safe technologies, organizations can build a resilient data protection framework that adapts to the dynamic nature of multi-cloud environments and emerging cyber threats.

5. Reimagine backup and recovery best practices
   Air-gapped backups, backup encryption, and automated recovery testing enhance data availability and integrity. The 3-2-1 backup rule—three copies of data, two on different media, one off-site—provides redundancy. Role-based access control and segregation of duties prevent insider threats. Identification of critical applications and having fail proof restoration plans as peris essential. Usage of automation and enterprise backup systems with support across hybrid multi-cloud environments makes the restore manageable.

Zero trust at work– Building a resilient and security-first culture

Enterprises that have embraced zero trust principles report improved compliance, reduced breach incidents, and enhanced operational efficiency. For instance, organizations using immutable storage and air-gapped backups have successfully thwarted ransomware attacks. Financial institutions leveraging AI for threat detection have significantly reduced fraud rates.

Operationalizing zero trust requires cultural and procedural shifts. Regular security training, strict IAM policies automated vulnerability scanning, AI driven data security monitoring and thread detection are essential. The key lies in aligning technology with governance and fostering a security-first mindset in data management across all levels of the organization.

Conclusion

In a world where cyber threats evolve as fast as innovation, data resiliency is not just an IT concern—it’s a business imperative. By adopting a zero trust security framework, organizations can safeguard their data against a wide spectrum of threats, from cyberattacks to quantum vulnerabilities. The integration of resilient storage, secure backup, and AI-driven cybersecurity measures ensures data continuity, availability, and integrity.

As multi-cloud architectures become the norm, businesses must proactively evolve their security strategies. The convergence of AI and quantum computing demands forward-thinking solutions that anticipate future risks. By operationalizing zero trust and embracing cyber resilience best practices, organizations can confidently navigate the complexities of multi-cloud environments and secure their most valuable asset—data.

References

AI is forcing the data industry to consolidate — but that’s not the whole story, Rebecca Szkutak, TechCrunch, July 7, 2025, https://techcrunch.com/2025/07/07/ai-is-forcing-the-data-industry-to-consolidate-but-thats-not-the-whole-story/

Your Cloud Strategy Is Now Your AI Strategy, Too, Lee Sustar, Forrester, April 1, 2024, https://www.forrester.com/blogs/your-cloud-strategy-is-now-your-ai-strategy-too/

Data Resiliency Across Multi-Cloud A Zero Trust Approach for Data Continuity, Amit Motiwale, Ashish Gulati, LTIMindtree, https://www.ltimindtree.com/wp-content/uploads/2025/04/Data-Resiliency-Across-Multi-Cloud.pdf