In today's data-driven world, protecting sensitive information has never been more crucial. As AI technology advances, the methods to secure data must evolve as well. This blog post delves into the concept of runtime data protection, particularly for large language models (LLMs), and introduces Blueteam Dreamcatcher's innovative approach to safeguarding data in real-time.
Background
Data Protection for AI
Data is the lifeblood of AI. Models evolve with advancements in hardware, but the data collected over time is the true asset. However, without robust data protection measures, this valuable data is at risk of being leaked, inadvertently used for training, or corrupted through data lineage poisoning. This makes effective data protection strategies paramount.
Static Data Protection
Traditional data protection methods, such as masking, tokenization, and synthetic data generation, provide some level of security. These techniques are designed for scenarios where data scientists work with static datasets. They allow for the anonymization of data to prevent the exposure of sensitive information. However, these methods fall short when applied to AI applications in a dynamic, real-time deployment environment.
Static data protection is a mature and well established practice with a large ecosystem of tools and techniques.
Some resources we have found useful include:
Runtime Data Protection
Runtime data protection addresses the challenges that static methods cannot. It is designed for environments where data is continuously streamed, making it impossible to observe all data at once. Additionally, it operates on the user experience (UX) critical path, requiring real-time processing. As models are deployed, data drift occurs, introducing novel categories that must be monitored and managed.
| Dimension | Static Data Protection | Runtime Data Protection |
|---|---|---|
| Definition | Protects data at rest, such as in databases and file systems. | Protects data in motion during processing and transmission. |
| Latency | Generally low impact on latency since data is not in transit. | Can introduce latency due to encryption and decryption processes. |
| Streaming | Not applicable; data is static. | Critical for streaming data; protection applied in real-time. |
| Data Drift | Not typically a concern, as data remains unchanged. | Must account for data drift to maintain accuracy and security over time. |
| Complexity | Easier to implement, as data does not change state frequently. | More complex, requiring dynamic protection mechanisms. |
Pain Point
Static data protection solutions struggle with the online, streaming, and continuously evolving nature of AI application deployments. They cannot provide the necessary protection in real-time, leading to potential data leaks and other security vulnerabilities.
Solution: Blueteam Dreamcatcher's Runtime Data Protection
Blueteam Dreamcatcher offers a cutting-edge solution to these challenges with its runtime data protection technology.
Streaming
Blueteam Dreamcatcher's approach does not require observing all data at once. Instead, it leverages a bootstrapped corpus of common protected data elements, enabling effective data protection even in streaming environments.
Latency
By employing model compression techniques such as distillation and quantization, Blueteam Dreamcatcher creates small, task-specific data protection models. These models operate in-band, ensuring that data protection occurs with minimal latency, maintaining an acceptable user experience.
Data Drift
One of the standout features of Blueteam Dreamcatcher is its ability to handle data drift. Through zero-shot learning, the system can adapt to novel categories without prior training, ensuring that even new, unexpected data elements are protected.
How It Works
Large language models are often used in a streaming mode where chunks of the
generated tokens are transmitted as they become available. This improves user
experience by reducing time to interactivity, but also complicates content
inspection as the chunks only represent small parts of the model's response.
One possible strategy is to buffer all of the chunks until the response is
complete, but doing so incurs the same latency penalty that streaming hoped to
mitigate and is not acceptable in many use cases.
In order to implement data protection while preserving streaming, Dreamcatcher
relies on the key insight that data protection violations can usually be
identified by only examining local context. For example, if you observe that
string begins with "I heard Bob went..." then you can already identify this constitutes
a PII violation regardless of what the suffix is.
To operationalize this insight, Dreamcatcher maintains a sliding window buffer
as chunks are streamed and at every new chunk first checks whether there is a
data protection violation within the sliding window before forwarding a chunk
through and shifting the window forwards.
| Chunk Number | Unprotected Stream | Sliding Window Contents | Stream with Data Protection |
|---|---|---|---|
| 1 | I | I | |
| 2 | heard | I heard | I |
| 3 | Bob | heard | heard |
| 4 | went | [NAME] went | [NAME] |
Notice that this significantly increases costs relative to buffering --
inference must be run every time a chunk arrives rather than just once after
the entire message is received. This constraint precludes many out of the box
models as their inference latency is too slow, but it is not a problem for
Blueteam Dreamcatcher because we have developed proprietary small models
which are single-language and task-specific and can operate at latencies sufficiently
fast to preserve a streaming and responsive user experience.
Call to Action
Do you have a large language model (LLM), either vendor-provided or self-hosted, that you need to protect from data leaks? With Blueteam Dreamcatcher, you can achieve robust runtime data protection in just a few simple steps. Add it as a new Advanced upstream, enable a Data Loss Prevention (DLP) policy, and configure it to suit your specific needs.
Book a demo and secure your LLMs today with Blueteam Dreamcatcher to ensure that your data remains protected in real-time.