Create Safe Generative AI Solutions Using AWS and Zscaler
Build Secure Generative AI Solutions with AWS and Zscaler
Generative AI is significantly transforming how businesses operate, from enhancing customer interactions to optimizing internal processes. However, this rapid evolution brings substantial security concerns. A notable rise in the use of AI/ML tools—36 times from the previous year—has led to heightened scrutiny, with security measures blocking 60% of traffic related to these technologies (Zscaler ThreatLabz 2025 AI Security Report). This article explores how AWS and Zscaler work in tandem to secure generative AI frameworks while promoting both productivity and compliance.
Understanding Generative AI and Its Security Necessities
Generative AI refers to algorithms that can create new content—text, audio, images—based on patterns learned from existing data. Given its significant capabilities, organizations can enhance efficiency but must also navigate the complexities of AI security. For instance, the OWASP Top 10 for Large Language Models highlights risks such as prompt engineering vulnerabilities and data leakage concerns.
The security requirements for AI applications are multifaceted and include not only securing the algorithms themselves but also managing data integrity and regulatory compliance. Failure to properly address these issues can lead to data breaches, compliance violations, and loss of customer trust.
Key Components of a Secure Generative AI Environment
Input Handling and Prompt Engineering
Every interaction with generative AI starts with user prompts, which can inadvertently lead to security vulnerabilities. Employees may introduce sensitive information into prompts, exposing organizations to dataleakage risks. Additionally, malicious actors could craft prompts aimed at manipulating AI models, posing significant threats.
To counter these risks, AWS and Zscaler implement a layered security approach. Amazon Bedrock Guardrails and Amazon Q moderation tools ensure that responses align with compliance requirements by filtering user prompts effectively. Zscaler’s AI Guard provides real-time monitoring of prompt interactions, blocking inappropriate or harmful requests before they can compromise the model. Together, these tools maintain a focused environment for AI, mitigating potential security breaches.
Model Access and Data Governance
With access to models and associated data, organizations need to prioritize robust governance mechanisms. Risks associated with model access include unauthorized use, improper data handling, and data validation issues during training processes. Zscaler employs Data Security Posture Management (DSPM) to monitor and secure sensitive data across cloud infrastructures. This includes identifying misconfigurations and unauthorized access attempts.
AWS enhances model security through Identity and Access Management (IAM), ensuring precise control over who can interact with nested AI resources. Features like encryption, API activity logging (via AWS CloudTrail), and private connectivity through Amazon Virtual Private Cloud (VPC) endpoints further secure sensitive data in generative AI applications.
Safeguarding Retrieval-Augmented Generation (RAG)
Retrieval-Augmented Generation (RAG) integrates external data with AI outputs, enhancing the relevance and accuracy of responses. However, its dependence on external sources introduces verification challenges. To counter this, the Zscaler Zero Trust Gateway ensures secure handling of data from the internet by scrutinizing incoming requests for security threats and blocking harmful connections in real time.
Similarly, AWS enables organizations to utilize Amazon Bedrock and Amazon Q for retrieving internal and trusted external information securely. By curating access through vetted internal repositories, organizations can ensure that RAG remains secure and reliable, minimizing the risk of accessing dangerous or unreliable online sources.
Governance and Regulatory Compliance
As companies increasingly turn to generative AI, adherence to regulatory frameworks—such as data protection laws regarding Personally Identifiable Information (PII)—is crucial. AWS provides tools like Amazon Bedrock Guardrails for maintaining adherence to behavioral standards in AI models. Zscaler complements these with continuous visibility into AI interactions, offering detailed audits and enforcing compliance policies dynamically based on content analysis and user behavior.
The Zscaler Data Loss Prevention (DLP) capabilities allow organizations to ensure that sensitive information remains protected during generative AI transactions, meeting regulatory standards and safeguarding against data breaches.
The Future of Secure AI: Agentic AI
As generative AI continues its evolution, the concept of agentic AI begins to emerge. These systems operate autonomously, capable of independent reasoning and decision-making. While offering substantial automation benefits, they also present new security challenges. To safeguard agentic AI, organizations must focus on prompt validation for agent interactions, reinforced access governance, and data protection protocols.
Adapting existing security measures to include these emerging technologies will be essential. Organizations can enhance their strategies by applying learned patterns from traditional AI security frameworks, ensuring a robust protective layer over their operations.
Action Steps for Organizations
To mitigate risks while harnessing the potential of generative AI, organizations should adopt a comprehensive security framework. This includes implementing AWS and Zscaler integrated solutions that can scale according to business needs, ensuring all stages of the AI lifecycle—from input ingestion to output generation—are adequately secured.
Companies should evaluate their current security posture and identify gaps in compliance, access control, and data protection. By establishing partnerships with trusted security providers like AWS and Zscaler, organizations can innovate confidently while navigating the complexities of generative AI. While the benefits of generative AI are substantial, ensuring that security remains a focal point will maximize its potential.
For those interested in collaborating with leading security solutions, contact Zscaler or explore their offerings on the AWS Marketplace.