As organizations increasingly move towards using private data with large language models (LLMs), security is critical. In fact, based on a recent MIT survey, the majority of respondents (59%) cited data governance, security or privacy as key concerns, while 48% highlighted challenges related to data integration. Building secure integrations from scratch is possible, but it demands expertise and significant time to set up and manage authentication, encryption, compliance, and more. Now, imagine doing that separately for every LLM your organization wants to use—the operational complexity quickly multiplies.
In this post, you will gain insight into the security must-haves and how Snowflake Cortex AI is built with these principles so that developers can focus on building applications with their preferred frontier models – whether it is one from Anthropic, OpenAI, Mistral, DeepSeek, or Meta.
Integrating your data with a large language model (LLM) requires careful consideration of several key security areas to protect sensitive information. Strong authentication, including multifactor authentication (MFA), is crucial as it prevents unauthorized access by adding an extra layer of security. Implementing robust access controls ensures that only authorized users and AI services can interact with the data. To secure data flows, establishing strong network security practices, ideally with a zero-trust architecture, is essential for maintaining control of the set of services authorized to see the data.
Protecting data at rest and in transit through encryption is another vital measure, safeguarding sensitive information from unauthorized access at any point. Building security monitoring and anomaly detection capabilities allows for consistent real-time checks for potential threats and provides audit trails for thorough investigations. To meet industry- specific requirements and mitigate legal risks, running compliance and certification checklists (such as SOC 2, ISO 42001, HIPAA, etc.) is necessary. Regularly applying security patches and vulnerability updates is also crucial for strengthening the system’s defences by keeping it current. Finally, establishing a rapid incident response framework enables swift and effective action to contain and resolve any security risks that may arise, while continuous penetration testing proactively identifies potential vulnerabilities, ensuring the system remains resilient against evolving threats.
Cortex AI provides a full range of industry-leading LLMs, alongside structured and unstructured data retrieval and orchestration services, for building AI data agents. Operating directly within Snowflake’s security perimeter, these services save valuable time on security setup and maintenance. Cortex AI offers comprehensive control, allowing developers to focus on developing and platform teams to easily and securely onboard more use cases.
Snowflake Cortex AI incorporates several key security measures to protect your data while leveraging LLM capabilities. For strong authentication when using the Cortex LLM REST API, key pair authentication is employed. Additionally, Snowflake supports multi-factor authentication (MFA) for human users accessing the platform, enhancing login security. These authentication methods can be further strengthened by pairing them with network policies to control the origin of traffic.
Access controls are streamlined through Snowflake’s unified, data-centric role-based access controls (RBAC) system, managing access to both data and AI resources at scale. Specifically, the snowflake.cortex_user database role allows granular control over which users can access the LLM functions. Model allowlists and RBAC policies are available if granular control over access to specific models is desired. Regarding network security, when using an LLM within the same region as your Snowflake account, the LLM remains fully contained within Snowflake’s secure boundary. Data transfer between the databases and the AI service is authenticated and encrypted using a zero-trust model. In cross-region scenarios, where a request might be sent to an LLM in a different Snowflake cloud region – e.g. your Snowflake account is on AWS US East 1 (N. Virginia) and the Cortex AI LLM is in Azure East US 2 (Virginia), data in transit is protected with mutual TLS 1.2 and above, using FIPS compliant algorithms.
Data encryption is implemented both at rest and in transit. Your data is encrypted client-side at rest with a unique key tied to your account in addition to using the cloud service provider’s service-side encryption. As mentioned above, data is also encrypted and authenticated in transit over untrusted networks using TLS 1.2 and above. For monitoring and logging, Snowflake’s threat detection team employs proprietary technology to identify security signals within logs, providing alerts for any anomalies discovered in the data. Furthermore, our support and engineering teams maintain on-call rotations to quickly resolve issues in case of performance drops or other identified incidents.
In terms of compliance, users benefit from all of Snowflake’s existing compliance certifications. Patch management and updates are handled through Snowflake’s sophisticated vulnerability management system, which scans first-party workloads and drives patching and remediation efforts in accordance with some of the strictest compliance regimes. For incident reporting, Snowflake’s dedicated incident response and threat detection teams have a robust system for handling security incidents that includes a set of prepared plans and regularly rehearsed scenarios. Finally, for continuous penetration testing, Snowflake complements its internal pentesting program with an open bug bounty program through HackerOne, proactively identifying potential vulnerabilities to maintain a resilient system against evolving threats.
Integrating LLMs and other AI services securely can be complicated and requires extensive configuration. Doing it yourself means handling complex workflows that can take time away from development or scaling use case deployment, with the added risk of non-uniform configurations and controls that could be exploited by malicious actors.
Snowflake Cortex AI provides you with secure access to multiple state-of-the-art LLMs. No single Cloud Service Provider can do that for you today. You just have to configure user authentication and access control to these models as you would do for any other Snowflake product. Behind the scenes, network security, data encryption, monitoring and logging, compliance, patch management, and so on are taken care of for you automatically by the Snowflake Secure Platform. This Secure LLM as a service allows you to focus on innovation rather than complex but extremely important security management.
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