Resources
Explore this section to learn more about necessity of protecting both traditional and generative AI models from various AI adversarial threats and how AI-powered cybersecurity solutions can empower enterprises to achieve a robust cybersecurity posture.
Protecting AI from Adversarial Attacks
As artificial intelligence (AI) technologies and particularly Generative AI (Gen AI) continue to evolve, so do the threats they face. Adversarial attacks, which pose significant risks to AI models, can result in data leakage, model theft, and manipulation.
This whitepaper explores the necessity of protecting both traditional and generative AI models from various AI adversarial threats including model inference, extraction, evasion and injection, data poisoning, prompt injections and personal identifiable information (PII) leakage.
We outline the emerging risks, the methodologies of these attacks, and propose a robust framework for mitigating these threats to maintain the security and integrity of AI systems.
Building a Future-Proof Enterprise Cybersecurity Posture with AI
In today's interconnected world, the threat landscape is constantly evolving, demanding proactive measures from organizations to safeguard their digital assets and maintain operational continuity. Cybersecurity teams are on the front lines of this battle, tasked with bolstering cyber resilience and mitigating cyber risks. To achieve these objectives, implementing strategic cybersecurity initiatives is crucial.
The ultimate objective of cybersecurity teams is therefore to implement cyber risk mitigations that result in a cyber resilient enterprise on top of insecure components.
Incorporating an understanding of cyber resilience in strategic planning is a key to implementing and operating an effective cybersecurity program.
This whitepaper delves into key initiatives enterprises can adopt to enhance enterprisecyber-resilience and reduce cybersecurity risks.
Artificial Intelligence has become an integral part of modern data-driven business processes, significantly contributing to innovation and efficiency. However, AI deployment carries substantial risks for data protection, compliance, and intellectual property that require robust security measures and clear governance frameworks.
Generative AI technologies including Large Language Models (LLMs) for text, diffusion models for images, and generators for audio, video, and multimodal content open diverse opportunities for companies to foster creativity and productivity.
AI supports automation, improves decision making, and optimizes operational processes across industries.
AI solutions often process highly sensitive information that must be comprehensively protected. The risk of unwanted internal access to confidential data through AI solutions like Microsoft Copilot or ChatGPT, combined with the threat of confidential company information being shared with external AI platforms, creates an urgent need for robust security measures.