CASB Alternatives for Governing Generative AI

As generative AI proliferates, traditional Cloud Access Security Brokers (CASBs) often fall short in comprehensive governance. This article explores dedicated alternatives and strategies for securing and controlling large language model (LLM) usage across the enterprise, identifying Bifrost as a leading solution for full-stack AI governance.

The rapid adoption of generative AI tools across enterprises has introduced novel security and governance challenges that often outpace the capabilities of existing infrastructure. While Cloud Access Security Brokers (CASBs) have been a cornerstone of cloud security, providing visibility and control over sanctioned SaaS applications and data in the cloud, their architecture and focus frequently struggle to keep pace with the unique demands of large language model (LLM) usage. Bifrost, an open-source AI gateway from Maxim AI, offers a more direct and comprehensive approach to governing generative AI traffic, from the central gateway to the individual endpoint.

The Limitations of Traditional CASBs in Governing Generative AI

Traditional CASBs were primarily designed to address challenges associated with SaaS application usage and data residency. They excel at monitoring and controlling access to known cloud services, enforcing data loss prevention (DLP) policies, and identifying shadow IT where unsanctioned cloud apps are in use. However, generative AI introduces several complexities that can bypass or overwhelm these established controls:

• Protocol and API Diversity: While many LLM interactions occur over standard HTTP/S APIs, the nature of the data (prompts and completions) and the rapid evolution of models and providers present a moving target. CASBs may struggle to deeply inspect and apply granular policies to these dynamic LLM conversations.
• Endpoint Proliferation (Shadow AI): Generative AI tools are increasingly deployed as desktop applications, browser extensions, and coding agents directly on employee machines. This "shadow AI" bypasses network perimeters and traditional CASB visibility, allowing sensitive data to flow directly from the endpoint to external LLM providers without organizational oversight.
• Focus on Known Services: CASBs typically operate with a predefined catalog of cloud applications. The landscape of LLM providers and specialized AI tools is vast and constantly expanding, making it difficult for CASBs to maintain comprehensive coverage and policy enforcement for every emerging AI service.
• Contextual Understanding of Prompts: Applying effective governance to generative AI requires understanding the intent and content of prompts and responses, not just blocking known file types. Traditional DLP capabilities in CASBs, while useful for structured data, may not be nuanced enough to detect IP leakage or sensitive information in natural language interactions without extensive customization.

These limitations highlight a significant gap in an organization's security posture, leaving sensitive data vulnerable and compliance at risk.

The Urgent Need for Dedicated Generative AI Governance

The uncontrolled proliferation of generative AI tools creates a new vector for critical enterprise risks, demanding a dedicated governance strategy.

• Data Leakage and IP Exposure: Employees feeding proprietary code, customer data, or internal strategies into public LLMs can lead to unintended data exfiltration and intellectual property loss.
• Compliance Violations: Industries subject to regulations like GDPR, HIPAA, SOC 2, or financial compliance can face severe penalties if sensitive customer or employee data is processed or stored by unapproved AI services without an audit trail.
• Unapproved Model Usage: Without governance, employees might use models that are unvetted for accuracy, bias, or data privacy, leading to unreliable outputs or the inadvertent spread of misinformation.
• Cost Sprawl: Ungoverned LLM usage can lead to unexpected and uncontrolled API costs, especially for high-volume or complex queries.
• Lack of Auditability: Most traditional security tools lack the granular logging and monitoring necessary to create an immutable audit trail of AI interactions, which is essential for incident response and compliance.

Organizations require visibility into what AI tools are being used, who is using them, what data is being shared, and how those interactions align with internal policies and external regulations.

Emerging Alternatives and Strategies for AI Governance

Addressing the gaps left by traditional CASBs for generative AI requires specialized approaches. Several categories of solutions are emerging to tackle this problem:

• Specialized AI Gateways: These act as an intelligent proxy layer for all LLM API traffic, centralizing routing, authentication, load balancing, cost management, and governance for prompts and responses.
• Endpoint AI Governance Agents: These are software agents deployed directly onto user devices to enforce policies locally, particularly for desktop AI applications, browser-based AI, and coding assistants that bypass network controls.
• Enhanced Data Loss Prevention (DLP) for LLMs: Some DLP solutions are evolving to better understand natural language, but still often focus on content scanning rather than full lifecycle governance of AI interactions.
• Dedicated AI Security Platforms: Broader platforms that combine elements of gateway functionality, endpoint control, and specific AI security features like prompt injection detection or model output validation.

For enterprises aiming for comprehensive control and visibility, a combination of specialized AI gateways and endpoint governance agents often provides the most robust solution.

Bifrost: A Comprehensive AI Gateway and Endpoint Governance Solution

Bifrost addresses the unique challenges of generative AI governance by acting as both a centralized AI gateway and an endpoint enforcement mechanism. It unifies access to over 1000 models through a single OpenAI-compatible API, while also extending crucial governance and security controls to every machine in an organization.

As an AI gateway, Bifrost functions as the central control plane, where administrators configure virtual keys, budgets, rate limits, routing rules, and audit logging. This centralized approach enables consistent policy enforcement across all AI applications configured to route through it. Organizations can implement automatic fallbacks and load balancing to ensure reliability and cost optimization across multiple LLM providers. Bifrost also functions as an MCP gateway, allowing for the secure and governed execution of external tools by AI agents.

Bifrost extends this powerful governance to the endpoint through Bifrost Edge. Bifrost Edge is an agent deployed on employee macOS, Windows, and Linux machines that routes all AI traffic from desktop applications, browser AI, and coding agents through the central Bifrost gateway. This critical component eliminates "shadow AI" by bringing otherwise ungoverned endpoint usage under the same policies configured in the Bifrost gateway.

Key capabilities of Bifrost Edge include:

• App Governance: Administrators can allow or deny specific AI applications (e.g., Claude Desktop, ChatGPT web, Cursor) across the fleet, with policies enforced directly on the device.
• MCP Governance: Edge provides unprecedented visibility into which Model Context Protocol (MCP) servers users have configured within their AI tools, enabling admins to approve or deny these external tool connections fleet-wide.
• Unified Guardrails: The same guardrails configured in Bifrost (e.g., secrets detection, custom regex for PII, AWS Bedrock Guardrails, Azure Content Safety) are automatically applied to endpoint AI traffic, protecting sensitive data before it leaves the machine.
• MDM Deployment: Designed for enterprise rollout, Bifrost Edge supports fleet-wide deployment via MDM platforms like Jamf, Microsoft Intune, and Kandji, simplifying adoption.

Bifrost, with Edge, provides full-stack AI governance that spans both server-side and client-side AI interactions, ensuring that an organization's security, compliance, and cost control policies apply consistently everywhere.

Other Approaches to Generative AI Governance

While Bifrost offers a unified gateway-plus-endpoint solution, other individual tools and strategies also contribute to the broader AI governance landscape.

• Network-Level Proxies and Firewalls: These can block access to known unsanctioned AI domains or apply basic content filtering. However, they lack the deep LLM context required for nuanced policy enforcement and cannot distinguish between approved and unapproved uses of the same AI service, nor can they govern desktop applications that bypass network proxies.
• Cloudflare AI Gateway provides caching, rate limiting, and observability for AI inferences, acting as an intelligent edge for LLM requests. It is a hosted solution, primarily focusing on network-level optimization and security for API calls.
• Kong AI Gateway offers an API management solution tailored for AI traffic, including features like prompt engineering, caching, and policy enforcement within the Kong ecosystem. Its strength lies in integrating AI governance into existing API gateway deployments.
• Data Loss Prevention (DLP) Software: Modern DLP solutions are evolving to identify sensitive data in prompts and responses, but they are typically reactive (blocking after detection) and may struggle with the sheer volume and variability of LLM interactions. They do not inherently provide the routing, load balancing, or endpoint app governance that dedicated AI gateways or agents offer.
• Specialized AI Security Platforms: Some platforms offer AI-specific threat detection and vulnerability scanning. While valuable for identifying risks within AI models and applications, they often do not provide the foundational infrastructure for traffic routing, policy enforcement across multiple providers, or endpoint control.

These alternatives address specific aspects of AI governance, but often require integration of multiple disparate tools to achieve comprehensive coverage, potentially leading to complexity and gaps.

Selecting the Right AI Governance Strategy

For enterprises, selecting the right generative AI governance strategy hinges on achieving comprehensive visibility, consistent enforcement, and scalability, while meeting compliance needs.

• Full Visibility: The solution must be able to see all AI traffic, regardless of whether it originates from a server-side application, a coding assistant, or a browser tab.
• Granular Control: The ability to set and enforce policies on who can use which models, what data can be shared, and at what cost is paramount.
• Compliance and Auditability: Robust audit logs and the ability to integrate with enterprise identity providers are non-negotiable for regulated industries.
• Ease of Deployment and Management: A solution that can be rolled out efficiently across a large fleet via existing MDM infrastructure and managed centrally reduces operational overhead.

A combined AI gateway and endpoint governance approach, like Bifrost and Bifrost Edge, provides a single pane of glass for configuring and enforcing policies, closing the shadow AI gap, and ensuring that all generative AI usage aligns with organizational requirements. This integrated strategy offers the control and visibility that traditional CASBs cannot natively deliver for the dynamic world of LLM interactions.

Teams evaluating AI gateways and endpoint governance can request a Bifrost demo or review the open-source repository for more information.

Sources

• The Register. (2023, November 13). CASBs struggle to grasp generative AI risk – report. https://www.theregister.com/2023/11/13/casbs_generative_ai_risk_report/
• Gartner. (2024, February 19). Security Threats of Generative AI. CASB for Generative AI Security. https://www.gartner.com/en/articles/security-threats-of-generative-ai
• Maxim AI. (n.d.). End Shadow AI with Bifrost Edge. https://www.getmaxim.ai/bifrost/edge
• IBM. (2023, August 3). The rise of shadow AI: how to manage it and reduce risks. https://www.ibm.com/blogs/research/2023/08/03/shadow-ai/
• Snyk. (2023, October 11). GenAI security and compliance: Mitigating new risks. https://snyk.io/blog/genai-security-compliance-mitigating-risks/