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Consus Implements Robust Compliance and Security Measures for Sensitive Data

Consus, a YC F24 startup, satisfies ITAR restrictions and completed its FedRAMP equivalency audit against 410 FedRAMP High controls, ensuring strict compliance for cloud services handling sensitive unclassified data.

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Compliance and Security Measures

Consus, a YC F24 startup, implements robust compliance and security measures to address the needs of companies handling sensitive data. The platform satisfies ITAR restrictions and has recently completed its FedRAMP equivalency audit against the 410 FedRAMP High controls, aligning with the government’s strictest security baseline for cloud services handling sensitive unclassified data. To ensure compliance with ITAR, Consus verifies every organization and establishes contractual agreements before issuing API keys. Additionally, Consus does not store any customer data at rest, and all prompts and completions remain in memory only for the duration of the request, never being logged or written to disk. The company also has contractual agreements in place to ensure that no data passing through its platform is used for model training purposes.

Challenges in AI Adoption for Sensitive Data

Many companies face significant hurdles in adopting AI due to data protection and compliance risks. As noted by industry insiders, compliance requirements often create barriers that hinder the use of new technologies and make it difficult to trust vendors handling sensitive data. This is particularly evident in sectors like aerospace and defense, where companies recognize the potential benefits of AI but are deterred by concerns over data security and regulatory compliance. For instance, some organizations are wary of model providers training on their data, which could lead to violations of rules such as ITAR or result in lost business due to failed cyber assessments. While solutions like GovCloud offer some level of control, they do not fully address the complexities of managing sensitive data across different AI model providers, as seen with the availability of models like GPT-5.6 Sol in Azure Government and Claude Opus 5.5 in AWS GovCloud.

Data Handling and Observability Challenges

Data handling and observability present significant challenges for companies using LLM gateways, particularly when aiming for zero data retention. As noted in the Consus documentation, ensuring that prompts do not leak into error logs, APM traces, or dead-letter queues is a complex task, even when the primary data path retains nothing. This highlights the difficulty of maintaining both privacy and visibility into system behavior. Consus addresses this by explicitly stating in its contracts that it will not use any data for training purposes, a commitment reinforced by the fact that it does not store any prompt data. These measures are critical for organizations handling sensitive information, as they help mitigate risks associated with data exposure. The company's approach to compliance and security is further detailed in its architecture and compliance documentation, offering transparency into how it enforces policies at the API level rather than through dashboard configurations.