Data Leak Prevention (DLP) for AI
Establish trust boundaries for AI model building and consumption.
Trusted by the most innovative brands
.svg){kind=small}
.svg){kind=small}
.svg){kind=small}
Enterprises need a secure way to innovate
Data exposure
AI models require high volumes of data, and are often exposed to sensitive company and customer data as a result.
Model training
AI models employ self-learning that is often difficult to control.
Human error
AI models are often deployed in environments where employees and customers alike can accidentally input sensitive data.
Enterprises encounter challenges with AI every day
Enterprises may use public LLMs like OpenAI to assist customer service agents as they respond to customer inquiries and troubleshoot issues. Customers often "over-share" sensitive information like social security numbers, credit card numbers, and more. That data may get transmitted by your service agents to OpenAI.
Enterprises often use OpenAI to debug code or for completion. If your code includes an API key, that key could be transmitted to OpenAI.
Enterprises could use OpenAI to moderate content sent by patients or doctors in an internally built health app. These queries may contain PHI, which could be transmitted to OpenAI, and pose a risk to compliance.
Learn more
about benefits
Duis vel morbi orci volutpat tellus. Gravida dolor pretium ut rhoncus tellus diam suspendisse ut.
Healthcare organizations need to protect PHI and comply with HIPAA. Nightfall automatically classifies all cloud data and finds at-risk patient data from a single platform.
.svg)
Use prebuilt, high accuracy detectors or create your own
Build detection rules for your use cases
Scan text and files, including images
Remediate sensitive data with redaction techniqu
The solution? Content filtering
The solution? Content filtering before API requests are sent to AI models
Create a detection rule with the Nightfall API or SDK client.
Send your outgoing prompt text in a request payload to the Nightfall API text scan endpoint. The Nightfall API will respond with any detected sensitive findings as well as the redacted payload.
Send the redacted prompt to the AI model using its API.
Leverage AI models safely in an enterprise setting
Empower users to leverage AI models without exposing sensitive data.
Track insider threats by monitoring downloads from SaaS apps to removable media.
Ensure compliance with data privacy laws and regulations.
Maximize productivity, without compromising on AI tool effectiveness, as AI models don't need sensitive data in order to generate a cogent response.
Investigate potential threats by viewing reports on specific users, including a list of files that any given user accessed, edited, or downloaded.
.svg)
.svg){kind=small}
.svg){kind=small}
