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Veeam’s Rick Vanover offers insights on data resilience and the key to AI and cybersecurity. This article originally appeared on Solutions Review’s Insight Jam, an enterprise IT community enabling the human conversation on AI.
The current conversations in the technology landscape are being driven by coverage of two topics: AI and cybersecurity. AI is the hot topic right now and has caught the attention of both industry leaders and media, resulting in a myriad of research initiatives like the CyberAI Project from Georgetown’s Center for Security and Emerging Technology, and bolstered security directives and cyber awareness efforts from the National Security Agency and the Cybersecurity and Infrastructure Security Agency.
While AI and cybersecurity continue to dominate news headlines, one critical aspect of digital infrastructure is often overlooked: data resilience. It is central to AI and cybersecurity success. By providing continuous availability and data integrity, data resilience reduces the risks of data loss and downtime, building the foundation for the dependability of these advanced systems. Understanding and emphasizing the role of data resilience is essential for a holistic and sustainable digital strategy.
Enhancing AI and Cybersecurity Through Data Resilience
Data resilience refers to an organization’s ability to protect, access, and recover its data during and after disruptions. It is indispensable for both AI and cybersecurity. AI systems thrive on vast data, requiring continuous availability and integrity to function effectively. Any blockage in the flow of data can lead to serious setbacks, diminishing the accuracy and reliability of AI predictions and decision-making. Similarly, effective cybersecurity measures depend on the availability of accurate and up-to-date data to identify threats, analyze vulnerabilities, and respond to incidents.
A resilient data infrastructure ensures that data is always available when needed, protected from loss or corruption, and quickly recoverable during a disruption. This robustness enhances the performance of AI algorithms by providing uninterrupted access to high-quality data and strengthens cybersecurity defenses by ensuring that critical data is safeguarded and recoverable.
Data resilience doesn’t only enhance AI, AI also contributes to data resilience through data intelligence, which involves using AI to extract deeper insights from vast data sets. This is achieved through AI-powered techniques like detection and security scoring, automatic diagnostics and remediation analysis, and repurposing backup data for mining and development testing. By providing more meaningful insights and additional data for AI training, these methods contribute significantly to an organization’s overall data resilience.
Common Misconceptions About Data Resilience
Backups are precious assets, but they also are prime targets for cyber-attacks. A 2024 Ransomware Report revealed bad actors target backup repositories in 96 percent of attacks, with the repositories being successfully affected in 76 percent of cases. This underscores data resilience’s significance.
Despite its importance, data resilience is often misunderstood. It’s more than simple data backup; it also includes data recovery, data freedom, data security, and data intelligence. Data resilience is often overlooked because it’s seen as a back-end concern and lacks the visibility of AI and cybersecurity innovations. This gap in understanding can lead to insufficient investment and prioritization within organizations.
Investment Prioritization Strategies
Organizations need to adopt a more integrated approach to a digital strategy where they recognize the interdependence of AI, cybersecurity, and data resilience. Prioritizing investment in data resilience involves several vital strategies:
- Assessment and Planning: Conduct thorough evaluations 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 drills, and continuous monitoring to prevent data loss and ensure quick 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: Teach teams and stakeholders about why data resilience is important and offer training on data resilience best practices and the newest technology and tools.
Educating Teams and Stakeholders
Education is crucial in shifting an organization’s perception and understanding of data resilience. When building a culture of data resilience, it’s important to educate 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 inspire wider support and interest.
As the technological landscape continues to evolve, the connection between cybersecurity, AI, and data resilience becomes clearer. Organizations can build a more robust, secure, and effective digital infrastructure by broadening strategic business imperatives to include data resilience and prioritizing its implementation. Data resilience is not just a supporting player; it is the foundation that enables AI and cybersecurity to reach their full potential, ensuring continuous innovation and protection in an ever-changing technological world.
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