Safeguard sensitive data across the AI stack
Nightfall is as a trust boundary that protects sensitive company and customer data during AI model building and consumption.
Trusted by the most innovative organizations
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Build security into your AI-powered apps from the ground up.
Data exposure
AI training and retrieval augmented generation (RAG) datasets may include sensitive company and customer information, leading to unintended data exposure.
Privacy breaches and noncompliance
Failing to protect sensitive customer data can result in legal issues, costly fines, and the loss of customer trust.
Prompt-based attacks
Threat actors can manipulate LLM behavior through prompts, leading them to bypass safety filters, disclose sensitive information, or generate harmful content.
Establish trust boundaries for AI model consumption.
Human error
AI models are often deployed in environments where employees and customers alike can accidentally “over-share” sensitive data.
Sensitive data disclosure
LLMs can inadvertently memorize and expose PII, PCI, PHI, secrets, and other sensitive data during training or inference, leading to data breaches and noncompliance.
Malicious threats
Threat actors can target AI models via data poisoning, jailbreaking, prompt injection, and other attacks in order to access sensitive company and customer data.
The solution? Content filtering
Sanitize LLM prompts and model outputs
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Create a detection rule with the Nightfall API or SDK client.
Send your prompts 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. Repeat the process for model outputs.
Nightfall's industry-leading detection accuracy and millisecond response times ensure that AI interactions are seamlessly secured.
Protect against adversarial attacks and create conversational guardrails
Detect and prevent adversarial attacks including prompt injection, jailbreaking, data poisoning, gibberish, invisible and block text, and malicious URLs.
Track insider threats by monitoring downloads from SaaS apps to removable media.
Add guardrails for conversation content, topics, code, languages, URLs, and more.
Identify dysfunctional conversations by checking LLM response refusal, user input sentiment, token limits, reading length, poorly constructed JSON, and more.
Investigate potential threats by viewing reports on specific users, including a list of files that any given user accessed, edited, or downloaded.
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Start securing AI usage now.
Create an API key and start scanning in minutes. No credit card required.
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