Cloud computing has been transformed by AI and machine learning (ML), which has improved performance, scalability, and efficiency. By using automation, anomaly detection, and predictive analytics, they help to optimize operations. However, an increased number of security threats are also associated with cloud computing because to the increasing accessibility and ubiquity of AI.
The risk of adversarial attacks using AI has increased with higher access to AI tools. A skilled adversary can produce inaccurate or misleading data by using evasion, poisoning, or model inversion attacks against machine learning models. The number of possible attackers with the ability to manipulate these models and cloud environments rises as AI tools become more widely used.
Due to the complexity, AI and ML models respond unpredictably in some situations, creating unexpected vulnerabilities.
The “black box” issue becomes worse by the increasing adoption of AI. The variety of uses and potential misuse of AI technologies increases with their wider availability, which expands the potential attack vectors and security risks.
What is more concerning, though, is that malicious actors have begun using AI to find malware and exploit cloud weaknesses. AI is a powerful weapon for cybercriminals because it may accelerate and automate the process of identifying vulnerabilities. They can identify vulnerabilities, analyze patterns, and take advantage of them more quickly than security teams by using AI. In addition, artificial intelligence can produce sophisticated malware that changes and learns how to avoid detection, making it difficult to eliminate.
These security issues have become more difficult by AI’s lack of transparency. The complexity of AI systems, particularly deep learning models, makes it difficult to identify and address security issues. The chance of these kinds of incidents increases as a wider user population now has access to AI.
The benefit of AI’s automation also brings with it a serious security risk: dependency. A security breach or malfunction in an AI system will have a greater impact as more services become dependent on AI. It is more difficult to identify and resolve this problem in the distributed cloud environment without disrupting with service.
The increasing use of AI also makes regulatory compliance more difficult. It becomes more difficult to comply with laws like the California Consumer Privacy Act (CCPA) and the General Data Protection Regulation (GDPR) when AI systems handle enormous volumes of data, including sensitive and personally identifiable information. The increased diversity of AI users increases the possibility of non-compliance, which could lead to severe penalties and damage to one’s reputation.
Steps to address cloud computing’s AI security issues
- Implement the Robust Access Management
- Access management is the most critical to securing the cloud environment. To ensure the security in cloud environment, you need to follow these points:
- Principle of least privilege,
- Provide minimum level of access to any user or application
- MFA must be enabled for all user
- Use Role based access mechanism to provide access to any user.
- Access management is the most critical to securing the cloud environment. To ensure the security in cloud environment, you need to follow these points:
- Enable the Encryption technology
- At Rest and Transit, data must be encrypted to ensure the protection from unauthorized access.
- Key Management System should be developed, and used for rotation and storing the keys securely.
- Use of IDS and Monitoring Tools
- Monitoring tools needs to be installed so that monitoring can be conduct for your cloud environment.
- Regular monitoring can be helpful to identify the potential threats and abnormal activities.
- Use of AI based IDS can enhance the monitoring capabilities in real time and provides you real-time threat analysis.
- Use of Agent-based technologies to automate the process of incident management.
- Conduct VAPT Assessment on periodic basis.
- Vulnerability Assessment should be conducted on regular basis so that potential weaknesses can be identified for your cloud environment.
- Conduct Penetration Testing (PT) to simulate the real-world attacks and measure the robustness of your implemented security systems for the cloud.
- Use of Cloud-Native Security Strategy
- Understand the Cloud Service Provider’s unique security features and tools.
- Use AWS Security Hub, Azure Security Center or Google Cloud Security Command Centre, all these services are native cloud security services