AI and machine learning (ML) have revolutionized cloud computing, enhancing efficiency, scalability and performance. They contribute to improved operations through predictive analytics, anomaly detection and automation. However, the growing ubiquity and accessibility of AI also expose cloud computing to a broader range of security risks.

Broader access to AI tools has increased the threat of adversarial attacks leveraging AI. Knowledgeable adversaries can exploit ML models through evasion, poisoning or model inversion attacks to generate misleading or incorrect information. With AI tools becoming more mainstream, the number of potential adversaries equipped to manipulate these models and cloud environments increases.

New tools, new threats

AI and ML models, owing to their complexity, behave unpredictably under certain circumstances, introducing unanticipated vulnerabilities. The “black box” problem is heightened with the increased adoption of AI. As AI tools become more available, the variety of uses and potential misuse rises, thereby expanding the possible attack vectors and security threats.

However, one of the most alarming developments is adversaries using AI to identify cloud vulnerabilities and create malware. AI can automate and accelerate finding vulnerabilities, making it a potent tool for cyber criminals. They can use AI to analyze patterns, detect weaknesses and exploit them faster than security teams can respond. Additionally, AI can generate sophisticated malware that adapts and learns to evade detection, making it more difficult to combat.

AI’s lack of transparency complicates these security challenges. As AI systems — especially deep learning models — are complex to interpret, diagnosing and rectifying security incidents become arduous tasks. With AI now in the hands of a broader user base, the likelihood of such incidents increases.

The automation advantage of AI also engenders a significant security risk: dependency. As more services become reliant on AI, the impact of an AI system failure or security breach grows. In the distributed environment of the cloud, this issue becomes harder to isolate and address without causing service disruption.

AI’s broader reach also adds complexity to regulatory compliance. As AI systems process vast amounts of data, including sensitive and personally identifiable information, adhering to regulations like the General Data Protection Regulation (GDPR) or the California Consumer Privacy Act (CCPA) becomes trickier. The wider range of AI users amplifies non-compliance risk, potentially resulting in substantial penalties and reputational damage.

Measures to address AI security challenges to cloud computing

Addressing the complex security challenges AI poses to cloud environments requires strategic planning and proactive measures. As part of a company’s digital transformation journey, it is essential to adopt best practices to ensure the safety of cloud services.

Here are five fundamental recommendations for securing cloud operations:

1. Implement strong access management. This is critical to securing your cloud environment. Adhere to the principle of least privilege, providing the minimum level of access necessary for each user or application. Multi-factor authentication should be mandatory for all users. Consider using role-based access controls to restrict access further.
2. Leverage encryption. Data should be encrypted at rest and in transit to protect sensitive information from unauthorized access. Furthermore, key management processes should be robust, ensuring keys are rotated regularly and stored securely.
3. Deploy security monitoring and intrusion detection systems. Continuous monitoring of your cloud environment can help identify potential threats and abnormal activities. Implementing AI-powered intrusion detection systems can enhance this monitoring by providing real-time threat analysis. Agent-based technologies especially provide advantages over agentless tools, leveraging the possibility to interact directly with your environment and automate incident response.
4. Regular vulnerability assessments and penetration testing. Regularly scheduled vulnerability assessments can identify potential weaknesses in your cloud infrastructure. Complement these with penetration testing to simulate real-world attacks and evaluate your organization’s ability to defend against them.
5. Adopt a cloud-native security strategy. Embrace your cloud service provider’s unique security features and tools. Understand the shared responsibility model and ensure you’re fulfilling your part of the security obligation. Use native cloud security services like AWS Security Hub, Azure Security Center or Google Cloud Security Command Center.

A new frontier

The advent of artificial intelligence (AI) has transformed various sectors of the economy, including cloud computing. While AI’s democratization has provided immense benefits, it still poses significant security challenges as it expands the threat landscape.

Overcoming AI security challenges to cloud computing requires a comprehensive approach encompassing improved data privacy techniques, regular audits, robust testing and effective resource management. As AI democratization continues to change the security landscape, persistent adaptability and innovation are crucial to cloud security strategies.