Overview
Generative AI (Gen AI) is revolutionizing industries with its ability to create human-like text, images, and code. However, this rapid advancement comes with unprecedented security challenges- data privacy risks, adversarial attacks, and compliance concerns that can compromise the integrity of AI-driven applications. Traditional security frameworks are no longer enough; organizations must adopt specialized security testing strategies to safeguard their Gen AI investments.
At QualiZeal, we understand that securing AI-powered systems requires a proactive and adaptive approach. From ensuring model robustness to preventing data leaks, our cutting-edge security testing methodologies help businesses fortify their Gen AI applications against evolving cyber threats.
In this whitepaper we cover:
- Key Security Threats in Gen AI – Understand the risks associated with AI-generated content, adversarial attacks, and biased outputs.
- Advanced Security Testing Strategies – Learn about innovative techniques like red teaming, AI model penetration testing, and bias detection.
- Best Practices for Secure AI Deployment – Get actionable insights on compliance, governance, and risk management to build resilient Gen AI applications.
