Veeam’s Rick Vanover offers insight into data resilience and the key to AI and cybersecurity. This article was originally published on Solutions Review Insight Jaman enterprise computing community enabling human conversation about AI.
Current conversations in the technology landscape are focused on covering two topics: AI and cybersecurity. AI is a hot topic today and has attracted the attention of industry leaders and the media, resulting in a myriad of research initiatives like the CyberAI project of Georgetown Center for Security and Emerging Technologiesand strengthened the security guidelines and cybersecurity awareness efforts of the National Security Agency and Cybersecurity and Infrastructure Security Agency.
As AI and cybersecurity continue to dominate headlines, a critical aspect of digital infrastructure is often overlooked: data resilience. It is a critical part of AI and cybersecurity success. By ensuring continuous availability and data integrity, data resilience reduces the risk of data loss and downtime, laying the foundation for the reliability of these advanced systems. Understanding and emphasizing the role of data resilience is essential for a holistic and sustainable digital strategy.
Improving AI and Cybersecurity with Data Resilience
Data resilience refers to an organization’s ability to protect, access and recover its data during and after disruptions. This is essential for both AI and cybersecurity. AI systems thrive on vast data, requiring continuous availability and integrity to operate effectively. Any blockages in the data flow can cause serious setbacks, decreasing the accuracy and reliability of AI predictions and decision-making. Likewise, effective cybersecurity measures depend on the availability of accurate and timely data to identify threats, analyze vulnerabilities and respond to incidents.
A resilient data infrastructure ensures that data is always available when needed, protected against loss or corruption, and quickly recoverable in the event of a disruption. This robustness improves the performance of AI algorithms by providing uninterrupted access to high-quality data and strengthens cybersecurity defenses by ensuring that critical data is protected and recoverable.
Data resilience not only enhances AI, AI also contributes to data resilience through data intelligence, which involves the use of AI to extract deeper insights from vast datasets. This is achieved through AI-based techniques such as security detection and assessment, automatic diagnostics and remediation analysis, and reuse of backup data for exploratory testing and of development. By providing more meaningful insights and additional data for AI training, these methods significantly contribute to an organization’s overall data resilience.
Common misconceptions about data resilience
Backups are valuable assets, but they are also prime targets for cyberattacks. A Ransomware Report 2024 found that bad actors target backup repositories in 96% of attacks, with repositories successfully affected in 76% of cases. This highlights the importance of data resilience.
Despite its importance, data resilience is often misunderstood. It’s more than just data backup; it also includes data recovery, data freedom, data security and data intelligence. Data resilience is often overlooked because it is considered a back-end concern and lacks visibility into AI and cybersecurity innovations. This lack of understanding can lead to insufficient investment and prioritization within organizations.
Investment Prioritization Strategies
Organizations need to take a more integrated approach to their digital strategy, recognizing the interdependence of AI, cybersecurity and data resilience. Prioritizing investments in data resilience involves several vital strategies:
- Assessment and Planning: Conduct in-depth assessments to identify potential risks and vulnerabilities in data management and develop comprehensive data resilience plans.
- Proactive measures: Implement proactive measures such as regular data backups, disaster recovery exercises, and continuous monitoring to prevent data loss and ensure rapid recovery.
- Technology Integration: Leverage advanced technologies and tools that integrate data resilience with AI and cybersecurity, creating a cohesive and robust digital infrastructure.
- Information and education: Explain to teams and stakeholders why data resilience is important and provide training on data resilience best practices and the latest technologies and tools.
Educate teams and stakeholders
Education is crucial to changing an organization’s perception and understanding of data resilience. When building a culture of data resilience, it’s important to train everyone from technical teams to executive decision-makers. Demonstrating real-world applications of data resilience and its positive effects on AI and cybersecurity can strengthen the message and generate broader support and interest.
As the technology landscape continues to evolve, the connection between cybersecurity, AI, and data resilience becomes clearer. Organizations can create a more robust, secure, and efficient digital infrastructure by expanding strategic business imperatives to include data resilience and prioritizing its implementation. Data resilience is not just a supporting player; it is the foundation that allows AI and cybersecurity to reach their full potential, ensuring continued innovation and protection in an ever-changing technological world.
