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by Shaunak Godbole

Cloud environments are rapidly adopting Artificial Intelligence (AI) and Machine Learning (ML) services, which presents both opportunities and challenges for various industries. While AI cloud services (e.g., Amazon SageMaker, Azure Cognitive Services, Azure OpenAI service, Vertex AI, etc.) enable organizations to drive innovation, improve efficiency, and enhance decision-making, they also introduce unique compliance and security risks.

To address these risks, a comprehensive cloud security risk assessment is necessary, focusing on identifying vulnerabilities, ensuring regulatory compliance, and implementing robust security controls.

The AI Service Risk Landscape

Unlike traditional cloud workloads, AI workloads carry specific risks that require a tailored security risk assessment.

These risks include:

• Model Integrity Risks: These risks involve attacks that manipulate the behavior of a model. Adversarial inputs, for example, can be used to misinterpret data which affects the functionality of AI systems.
• Training Data Risks: The exposure of personal identifiable information (PII) or proprietary data used in model training can lead to significant privacy, legal, and ethical risks.
• Inference Risks: An AI model, especially one trained on sensitive data like healthcare records, can inadvertently reveal information about its training data through its responses. This can happen even if the data was anonymized.

Cloud-Specific Risks

The cloud environment poses additional risks for AI services:

• Multi-Tenant Exposure: In a shared infrastructure, attackers can move between compromised workloads, potentially escalating access controls and leading to unauthorized access to sensitive data and systems.
• API Security: AI service endpoints with insufficient authentication or authorization can introduce API vulnerabilities. The OWASP API Top 10 vulnerabilities list includes issues like Broken Object Level Authorization, Broken Authentication, and Server-Side Request Forgery (SSRF).
• Data Residency and Sovereignty: This risk arises when data storage locations do not align with jurisdictional requirements. For example, storing patient scans for a radiology AI tool in a data center outside the U.S. could possibly violate HIPAA data residency rules. HIPAA mandates that Protected Health Information (PHI) be handled in accordance with U.S. privacy and security standards, t and a violation could lead to civil penalties and reputational damage.

Adapting Your Risk Assessment Framework

A comprehensive assessment framework for AI cloud services should be integrated into existing processes. Key adaptations for assessing AI/ML services in the cloud include:

• Scoping the Assessment: Ensure visibility across all deployed AI/ ML services, including “shadow IT”. It is also important to account for multi-cloud and hybrid environments, because different cloud service providers have varying security postures.
• Threat Modeling: Incorporate cloud-specific threat frameworks like MITRE ATT&CK for Cloud or the Cloud Security Alliance (CSA) Cloud Controls Matrix (CCM). This allows you to model risks such as misconfigured storage buckets and insecure API endpoints.
• Continuous Monitoring: Traditional point-in-time audits are not enough for the cloud. Organizations should adopt continuous compliance monitoring and integrate Security Information and Event Management (SIEM) and Cloud Security Posture Management (CSPM) solutions.

Best Practices for Integrated Security Assessments

To effectively manage these risks, organizations should leverage both cloud-specific and AI-specific practices.

Organizational Scope

• Use Native CSP Security Tools: Utilize built-in security platforms from cloud providers, such as AWS Security Hub or Azure Security Center, for real-time threat detection and visibility.
• Adopt Cloud-Specific Frameworks: Align assessments with standards such as the CSA Cloud Controls Matrix, NIST SP 800-53 (cloud-adapted), and ISO 27017.
• Prioritize Identity Security: Implement strong authentication, conditional access policies, and session monitoring.
• Automate Compliance Checks: Use Infrastructure as Code (IaC) scanning and CSPM solutions to continuously validate configurations.

AI-Specific Practices

• Adopt a Zero-Trust Approach: Treat AI endpoints as untrusted by default and enforce strict access controls.
• Conduct Regular AI Model Audits: Evaluate models for bias, drift, and vulnerabilities to ensure ethical and secure outcomes.
• Use Privacy-Enhancing Technologies (PETs): Apply techniques like federated learning and differential privacy to protect sensitive information during training and inference.
• Ensure Vendor Risk Management: Assess the security practices and contractual obligations of cloud providers and their third-party integrations to mitigate supply chain risks.

Conclusion

By embedding AI-specific risk considerations into existing cloud assessment frameworks, organizations can better protect sensitive assets, ensure compliance, and maintain trust in AI-driven outcomes. This structured methodology enables proactive risk management, vulnerability remediation, and responsible innovation in cloud-based AI deployments.

At Meditology Services, our Cloud Security Team specializes in comprehensive assessments designed to strengthen the security posture of healthcare organizations leveraging AI/ML cloud services. We deliver customized, actionable recommendations that align with your unique environment while ensuring compliance with critical industry standards such as HIPAA, HITRUST, and NIST.

Our proprietary Cloud Security Risk Register and Reporting framework empowers organizations to proactively manage risks, maintain regulatory alignment, and stay ahead of emerging cyber threats. With continuous updates to security controls, we help you harness the full potential of cloud technologies—securely and confidently.

Ready to evaluate and elevate your cloud security strategy? Partner with Meditology and take the first step toward resilient, compliant, and future-ready AI/ML cloud infrastructure.

About the Author

Shaunak Godbole is a seasoned Cloud Security Architect and Team Lead at Meditology Services, LLC, holding a Master’s (MS) degree in Computer Science. With certifications in Microsoft Azure Fundamentals and Solutions Architect Expert, Shaunak brings over six years of specialized experience in cloud security and risk management.

He leads Meditology’s Cloud Security Service Line, playing a pivotal role in its development and strategic direction. As a trusted engagement leader, Shaunak has successfully delivered security and compliance solutions to major healthcare providers across the country.

His technical expertise spans key regulatory frameworks including HIPAA, NIST, and HITRUST, positioning him as a recognized subject matter expert in IT security and compliance. Through his hands-on contributions, Shaunak has helped healthcare organizations strengthen their security posture and achieve regulatory compliance.

With a growing reputation in the healthcare cloud security space, Shaunak continues to advance as a thought leader, driving innovation and excellence in the field.

Resources

• https://owasp.org/API-Security/editions/2023/en/0x11-t10/
• https://www.sans.org/blog/securing-ai-in-2025-a-risk-based-approach-to-ai-controls-and-governance
• https://learn.microsoft.com/en-us/azure/cloud-adoption-framework/scenarios/ai/secure