The Security Gaps in AWS Bedrock & Azure AI You Need to Know

The Security Gaps in AWS Bedrock & Azure AI You Need to Know

by Cloud Security Podcast

Applied Sciences & Engineering Computing & Cybersecurity UComputing and Information TechnologyURComputer security
🎙 Cloud Security Podcast 👥 39K 📅 September 23, 2025 ⏱ 55 min 👁 716 📄 expert opinion 🧭 2026-08-16
Available in: English (current) Français

Keywords

AWS BedrockAzure AIsecurity gapscloud securityAI security

Summary

In this episode of the Cloud Security Podcast, hosts Ashish Rajan and Shilpi Bhattacharjee are joined by Kyler Middleton and Sai Gunaranjan, both from Veradigm, to discuss the security realities of building AI applications on AWS and Azure. The conversation covers common AI use cases in healthcare, such as chatbots and transcription services, and highlights the importance of moving beyond IAM to consider model selection, data pipelines, and content filtering. The guests share architectural patterns for AI on both clouds, including knowledge bases, vector databases, and serverless functions. They delve into specific security gaps: Azure AI’s default to send data globally for processing, which poses compliance risks, and AWS Bedrock’s lack of resource-level security policies and consolidated logging that complicates incident response. The discussion also touches on the risks of agentic AI, where models with write permissions can cause unintended harm, and the need for human-in-the-loop oversight. The episode concludes with a maturity model for adopting AI security and advice for cloud security engineers transitioning to AI security roles.

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Critical Evaluation

Value of the Information & Strength of the Argument

The value of the information is high for practitioners, as it provides real-world insights into security gaps that are often overlooked. The argumentation is based on direct experience, making it credible, though it lacks formal evidence or references. The hosts and guests effectively argue that cloud providers’ defaults are not secure by default, and that organizations must take proactive measures to secure AI deployments.

Scientific Rigor, Source Quality, Title Accuracy

The scientific rigor is moderate; the discussion is expert opinion rather than peer-reviewed research. The sources cited are limited to the podcast’s own website and social media, with no direct references to official AWS or Azure documentation. The title accurately reflects the content, focusing on security gaps in AWS Bedrock and Azure AI. No comments were provided for analysis.

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Title / Content Match

The title accurately reflects the content, which focuses on security gaps in AWS Bedrock and Azure AI services.

Quality & Reliability

7/10

The discussion is based on practical experience from two cloud architects working in the healthcare sector, providing concrete examples and insights. However, it is largely anecdotal and lacks formal citations or references to official documentation, which limits its verifiability.

Chapters

Cited Sources

Concurring Sources

  • AWS Bedrock Security Documentation — Official AWS documentation on Bedrock security, which may confirm or contradict the claims made in the episode.
  • Azure AI Services Security Documentation — Official Microsoft documentation on Azure AI security, which may provide additional context.

Dissenting Sources

  • AWS Bedrock Resource Policies — The episode claims that AWS Bedrock lacks resource-level security policies, but AWS documentation suggests that IAM policies can be applied at the resource level. This discrepancy may be due to the specific context or limitations encountered by the speakers.

Contribution & Novelties

The episode provides a practical comparison of security gaps in AWS Bedrock and Azure AI, based on hands-on experience in a regulated industry. It highlights specific insecure defaults and architectural challenges that are not widely discussed in official documentation. The discussion offers actionable insights for cloud security engineers moving into AI security.

Pour aller plus loin :

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Radar Profile

The radar profile shows a balanced distribution across all dimensions, with slightly lower scores in 'fiabilite_globale' due to the lack of formal citations. The high scores in 'quantite_information' and 'niveau_technique' reflect the detailed technical discussion, while 'qualite_information' is strong due to practical insights.

Reliability 6/10