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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.

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Why Cloud Data Protection is Essential for Addressing Modern Security Challenges

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Build security into your AI-powered apps from the ground up.

Enterprises cycle through vast quantities of data for model training, annotation, and fine-tuning. It’s essential to pinpoint and protect sensitive data at each of these key stages.
Nightfall for SaaS

Data exposure

AI training and retrieval augmented generation (RAG) datasets may include sensitive company and customer information, leading to unintended data exposure.

Nightfall for ChatGPT

Privacy breaches and noncompliance

Failing to protect sensitive customer data can result in legal issues, costly fines, and the loss of customer trust.

Nighfall for data at rest

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.

Third-party LLM providers help enterprises to power chatbots, virtual assistants, and automation pipelines that drive innovation and enhance customer value. However, they’re not without risk.
Nightfall for SaaS

Human error

AI models are often deployed in environments where employees and customers alike can accidentally “over-share” sensitive data.

Nightfall for ChatGPT

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.

Nighfall for data at rest

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.

Enterprises encounter challenges with AI every day

Nightfall Data Exfiltration Prevention leverages GenAI for benefits such as…

Data exposure

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.

Model training

Enterprises often use OpenAI to debug code or for completion. If your code includes an API key, that key could be transmitted to OpenAI.

Human error

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.

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HIPAA reporting and monitoring made easy

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.

  • 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

Nightfall Data Exfiltration Prevention leverages GenAI for benefits such as…

Get actionable insights in near-real time

Sanitize LLM prompts and model outputs

  • 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.

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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.

Get actionable insights in near-real time
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