The 2024 State of AI Infrastructure Report by Flexential reveals growing anxiety among IT leaders over the risks associated with AI investments. According to the report, nearly all IT executives believe AI is expanding the attack surface, amplifying vulnerabilities, and exposing organizations to greater cyber threats.
The survey, which included 350 IT leaders from organizations with over $100 million in annual revenue, highlights key challenges related to data protection, disaster recovery, and the need for resilient infrastructure to support AI deployments. It underscores that as AI becomes more integrated into business processes, organizations must evolve their security and disaster recovery strategies to match AI’s complex infrastructure needs.
The Growing Attack Surface of AI Applications
One of the most significant concerns highlighted by the report is that 54% of respondents believe AI applications inherently create a larger and more vulnerable attack surface. This is not surprising, given the sophistication and complexity of AI workloads, which often require vast amounts of data and computational resources to operate effectively.
As AI becomes more embedded in daily business operations, sensitive data like personal identifiable information (PII) and intellectual property are more frequently processed and stored across various systems. This increases the organization’s risk exposure, especially when cybersecurity measures are insufficiently aligned with the specific needs of AI systems. In fact, 39% of IT leaders surveyed indicated that they are now processing and storing more sensitive data due to their AI investments.
The Shift from Public Cloud to Colocation
To mitigate some of these concerns, organizations are increasingly moving AI applications from public cloud environments to colocation facilities. The report reveals that 42% of companies have made this strategic shift due to concerns over data privacy and security in public cloud infrastructures. By moving sensitive workloads to colocation or private clouds, organizations can exert more control over their data, ensuring compliance with strict regulatory requirements like GDPR and HIPAA.
This shift also helps address one of the most pressing security issues: protecting AI workloads from external threats. Colocation environments offer physical security, predictable performance, and robust disaster recovery capabilities, which are essential for maintaining operational resilience.
Cybersecurity Teams Struggling to Keep Up
Despite increased AI adoption, there is a concerning gap in the ability of cybersecurity teams to protect AI applications. The report found that 40% of respondents admitted their cybersecurity teams lack a deep understanding of how to secure AI workloads. This highlights a significant skills gap that could be exploited by cyber attackers, especially given the specialized nature of AI systems.
AI’s reliance on vast data sets and complex algorithms makes it challenging for traditional cybersecurity measures to provide adequate protection. AI models can be tampered with through adversarial attacks, where malicious actors manipulate input data to deceive the AI system into making incorrect predictions. Moreover, sensitive data fed into these models may be exposed to leakage or unauthorized access if appropriate safeguards are not in place.
Strengthening Disaster Recovery and Resilience
As AI infrastructure becomes more integral to operations, downtime or breaches can lead to significant disruptions. This is why Flexential’s report stresses the importance of having comprehensive disaster recovery (DR) strategies in place. Resilient infrastructure, regular testing of disaster recovery plans, and ensuring uptime are essential components for maintaining the integrity and performance of AI systems.
Disaster recovery must go beyond traditional measures to incorporate the specific needs of AI workloads, which often rely on real-time data processing and machine learning models that require continuous operation. Flexential’s Chief Innovation Officer, Jason Carolan, emphasizes that organizations need resilient, scalable infrastructure solutions to manage the high-performance demands of AI while minimizing the risk of cyber threats.
Improving Detection and Response Capabilities
A robust disaster recovery strategy also requires organizations to enhance their detection and response capabilities. According to Will Bass, VP of Cybersecurity Services at Flexential, organizations need to go beyond basic cybersecurity protections to ensure they can quickly detect and mitigate AI-related threats. By embedding advanced detection mechanisms and response protocols into the infrastructure that supports AI deployments, businesses can mitigate the impact of cyber incidents and maintain the integrity of their AI operations.
In particular, AI can also play a role in bolstering security by enabling faster identification of anomalies through machine learning models that detect patterns indicative of cyber attacks. By integrating AI into security operations, businesses can streamline threat detection and response times, reducing the likelihood of prolonged exposure to breaches.
Key Takeaways for IT Leaders
Flexential’s 2024 report serves as a wake-up call for organizations rapidly expanding their AI capabilities. As AI systems grow in complexity and size, they introduce new risks that IT leaders must address to ensure cybersecurity resilience.
Key recommendations include:
- Strengthening Disaster Recovery Plans: Ensure AI-specific workloads are included in disaster recovery planning, with regular testing to confirm resilience.
- Investing in Training: Cybersecurity teams must be upskilled to protect AI systems from evolving threats. A deeper understanding of adversarial attacks and the specific vulnerabilities of AI models is crucial.
- Transitioning Sensitive Workloads: For companies concerned about cloud security, transitioning AI workloads to colocation environments can enhance control over sensitive data.
- Enhancing Detection and Response: Integrating AI into security operations for real-time anomaly detection can significantly improve response times during attacks.
In conclusion, as AI adoption continues to rise, so too do the challenges associated with protecting these systems. Organizations must take proactive steps to safeguard their AI investments by aligning cybersecurity, disaster recovery, and resilience strategies with the unique demands of AI infrastructure.