AI Security

Focusing on the protection of AI systems from malicious actors and the ethical use of AI models.

1. Adversarial Attack Simulation (Red Teaming)

Goal: Proactively test models against sophisticated prompt injection and data poisoning attacks.

• Workflow:
  1. Systematically testing different attack vectors (e.g., DAN-style prompts, indirect injections).
  2. Evaluating the model’s resilience and identifying weak points in its alignment.
  3. Implementing input-sanitization and robust safety filters based on findings.
• Impact: Prevents model manipulation and unauthorized bypass of safety guardrails.

2. PII Masking & Data Privacy Guardrails

Goal: Prevent sensitive information from being processed by LLMs or stored in insecure logs.

• Workflow:
  1. Middleware layer that automatically detects and masks Personally Identifiable Information (PII) before it reaches the model.
  2. Zero-trust architecture for training and fine-tuning datasets.
  3. Ensuring compliance with regulations like GDPR and CCPA.
• Impact: Protects user privacy and maintains institutional trust.

3. Model Inversion & Extraction Defense

Goal: Prevent attackers from stealing model parameters or reconstructing sensitive training data.

• Workflow:
  1. Rate limiting and monitoring for unusual API query patterns.
  2. Implementing Differential Privacy (DP) techniques during training to prevent data leakage.
  3. Using watermarking and obfuscation techniques to deter model copying.
• Impact: Secures intellectual property and prevents large-scale data breaches.