Claire Dubois

Posted on Jun 24

CASB vs. DLP vs. AI Gateway: Which One Actually Controls AI

#aigateway #security #governance #dlp

As enterprises adopt generative AI, security teams are tasked with governing tools like Claude and ChatGPT. This article compares Cloud Access Security Brokers (CASBs), Data Loss Prevention (DLP), and AI Gateways, explaining why an Bifrost AI gateway is the most effective control layer for modern AI workloads.

The rapid adoption of generative AI has created a significant challenge for enterprise security and governance teams. When employees use tools like ChatGPT, Claude, and Gemini, they often handle sensitive data, from customer PII to proprietary source code. A 2025 LayerX report found that 77% of employees paste data into generative AI tools, creating a substantial risk of data exposure. To manage this, organizations are evaluating existing security stacks, primarily Cloud Access Security Brokers (CASBs) and Data Loss Prevention (DLP) solutions, and comparing them to a new category: the AI Gateway.

While CASBs and DLP play roles in the broader security landscape, they were not designed for the specific challenges of AI. Understanding the architectural differences between these three is crucial for choosing an effective control. An AI gateway like Bifrost, an open-source gateway from Maxim AI, provides the specific, granular control needed to govern AI traffic directly.

What is a Cloud Access Security Broker (CASB)?

A Cloud Access Security Broker (CASB) is a security policy enforcement point that sits between an organization's users and cloud service providers. Its primary role is to extend security controls beyond the on-premises network, providing visibility and governance for cloud applications (SaaS, PaaS, IaaS).

CASBs work by discovering which cloud applications are in use (including "shadow IT"), assessing their risk, and enforcing access policies. They typically offer four main pillars of functionality:

Visibility: Identifying all cloud services being accessed by users.
Compliance: Ensuring cloud data handling complies with regulations like GDPR and HIPAA.
Data Security: Applying policies like encryption and access restrictions to data in the cloud.
Threat Protection: Detecting anomalous user behavior and defending against cloud-based threats.

Where CASBs Fall Short for AI Governance

CASBs are application-centric, not data-flow-centric. They are effective at answering the question, "Is a user allowed to access ChatGPT?" but struggle with the more important question, "What is the user doing inside ChatGPT?"

A CASB can block or allow access to chatgpt.com, but it generally lacks the ability to inspect the content of an AI prompt in real time. It cannot differentiate between an employee asking for a recipe and one pasting a confidential customer list. Because it operates at the application access layer, a CASB has limited visibility into the conversational, contextual nature of AI interactions.

What is Data Loss Prevention (DLP)?

Data Loss Prevention (DLP) is a set of technologies and processes designed to prevent the unauthorized exposure of sensitive data. Unlike a CASB, which governs access to applications, a DLP solution is data-centric. It works by identifying, monitoring, and protecting data whether it is at rest, in motion, or in use.

DLP solutions typically function by:

Discovery & Classification: Scanning data across endpoints, networks, and cloud storage to identify sensitive information based on keywords, patterns (like credit card numbers), or data fingerprints.
Monitoring: Observing how data is being used and moved throughout the organization.
Enforcement: Applying policies to block or alert on unauthorized actions, such as emailing a sensitive file or uploading it to an unapproved web service.

Where DLP Falls Short for AI Governance

Traditional DLP solutions face architectural challenges when applied to generative AI. Network-based DLP struggles because much AI traffic is encrypted (HTTPS), and endpoint DLP can be bypassed by users switching to personal devices or browser tabs where corporate agents have no control.

More fundamentally, DLP was built for known data patterns moving through predictable channels like email or file transfers. AI prompts are different; they are dynamic, conversational, and often contain sensitive context without matching a simple regex pattern. A specialized "AI DLP" has emerged to address this, operating closer to the browser or prompt layer, but this often requires separate agents and policies distinct from the core infrastructure. Legacy DLP tools simply lack the context to understand the intent behind an AI interaction.

What is an AI Gateway?

An AI Gateway is a specialized middleware layer that sits between users or applications and the AI models they access. It acts as a central control plane to manage, secure, and observe all AI traffic, regardless of which model or provider is being used. Unlike CASBs or DLPs, an AI gateway is purpose-built for the unique characteristics of AI workloads, such as token-based pricing, streaming responses, and agentic interactions.

Bifrost, for instance, provides a unified, OpenAI-compatible API for over 20 providers. This centralizes all AI requests through a single point where fine-grained policies can be applied.

How an AI Gateway Delivers Superior Control

An AI gateway provides the specific controls that CASBs and DLP lack for AI governance.

Prompt and Response Inspection: An AI gateway can inspect the full content of every prompt and response in real time. This allows it to enforce content-based guardrails, such as blocking prompts containing PII or redacting sensitive data from model outputs before they reach the user.
Granular Access Control: Instead of coarse, application-level blocking, an AI gateway uses mechanisms like virtual keys to implement granular policies. An administrator can define rules per-user, per-team, or per-application, controlling which models can be used, setting budgets, and enforcing rate limits. For example, a marketing team could be granted access to creative models while an engineering team is routed to models optimized for code generation.
Cost Management and Observability: AI usage is measured in tokens, a metric that traditional security tools do not track. An AI gateway provides detailed observability into token consumption, enabling precise cost attribution and the enforcement of spending limits to prevent runaway costs.
Resilience and Routing: AI gateways can automatically route requests between different models or providers based on cost, performance, or availability. If a primary model provider experiences an outage, a gateway like Bifrost can execute an automatic fallback to a secondary provider, ensuring application resilience.
Audit and Compliance: By logging every request and response, an AI gateway creates an immutable audit trail essential for compliance with regulations like SOC 2, HIPAA, and GDPR. This detailed record shows exactly what data was sent to which model, by whom, and what the response was.

Extending Governance to the Endpoint with Bifrost Edge

The final piece of the AI control puzzle is the "last mile"—the employee's machine, where most shadow AI usage occurs. An AI gateway governs configured traffic, but it cannot control an employee pasting data into a personal ChatGPT account from their browser.

This is where a solution like Bifrost Edge completes the picture. It is an endpoint agent that extends the gateway's governance policies directly to the user's desktop and browser. It transparently routes all AI traffic—from desktop apps like Claude and from browser sessions—through the central Bifrost AI gateway, ensuring that the same security, governance, and audit controls apply everywhere, even on unconfigured applications.

Conclusion: The Right Tool for the Job

While CASBs and DLP solutions are valuable components of a layered enterprise security strategy, they are insufficient for controlling generative AI. CASBs manage access to cloud apps, and DLP protects known data patterns. Neither was designed to govern the dynamic, contextual, and API-driven nature of AI interactions.

An AI gateway is the purpose-built tool for this new challenge. It provides the deep visibility, granular control, and AI-native features required to manage security, cost, and compliance effectively. For organizations serious about enabling AI productivity while mitigating risk, implementing an AI gateway is not just an option; it is a necessity.

Teams evaluating AI control solutions can request a demo of Bifrost to see how a dedicated AI gateway provides comprehensive governance or review the open-source repository.

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