AI Data Security
The rapid adoption of Large Language Models (LLMs) has outpaced
the development of strong security measures, leaving many
enterprise applications exposed to significant risks.
To address these concerns, OWASP has created the Top 10 list for
LLM Applications, guiding safer adoption and implementation by
focusing on key security risks and vulnerabilities.
Key Data Security Risks
With LLMs
Sensitive Information Disclosure (LLM06)
LLMs may unintentionally reveal confidential data through their responses, leading to privacy violations and unauthorized access.
Training Data Poisoning (LLM03)
Training Data Poisoning (LLM03) Malicious manipulation of training data can introduce vulnerabilities or biases, compromising the model’s security and integrity.
Insecure Output Handling (LLM02)
Inadequate validation and sanitization of LLM outputs may expose backend systems to potential risks.
Overview
LLM applications may expose sensitive information, proprietary algorithms, or confidential data through their outputs, risking unauthorized access and privacy breaches. Safe interaction with LLMs is crucial to avoid unintentionally disclosing sensitive data.
Key Security Risks in LLM
Applications
Vulnerabilities
Improper filtering of sensitive information in LLM responses.
Memorization of sensitive data during training.
Unintended disclosure of confidential data due to poor data scrubbing.
Inadequate input validation by third-party plugins.
Attack Surfaces
Users input sensitive data, which may be reflected in outputs to others or inadvertently exposed to legitimate users.
Crafted prompts bypass input filters, revealing sensitive information.
Personal data leaks from improper training data handling.
Web apps generate content from prompts without sanitization.
How Privaclave Safeguards Data Security for GenAI/LLM Applications
- Transparent, non-invasive, runtime solution for real-time sensitive data detection & classification, and sanitization.
- Prevents sensitive data from entering the LLMs for training the models, even during refinement.
- Implements strict data-centric controls across all stages—pre-training, fine-tuning, and embedding.
- Effectively protects sensitive information throughout the entire process, even during LLM output handling.
Click on the Graphic to Watch a Demo
Privaclave: Leading the Future of AI and LLM Data Security
- Real-time interception, detection, classification, and sanitization of data inputs and outputs.
- Non-invasive design for AI-driven, run-time data classification and Protection.
- Uses industry-standard cryptographic methods for enhanced security.
- Ensures high performance and scalability for Generative AI and LLM applications.
- Empowers enterprises to innovate freely while practicing responsible AI
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