Название: Privacy in the Age of Innovation: AI Solutions for Information Security Автор: Ranadeep Reddy Palle, Krishna Chaitanya Rao Kathala Издательство: Apress Год: 2024 Страниц: 205 Язык: английский Формат: pdf (true), epub, azw3, mobi Размер: 10.1 MB
This book will help you comprehend the impact of Artificial Intelligence (AI) on information security, data privacy, and data security.
The book starts by explaining the basics and setting the goals for a complete understanding of how AI, Information Security, Data Privacy, and Data Security all connect. Then, it gives you important information about the basics of AI, Machine Learning, and Deep Learning in simple terms. It also talks about the ethics of using AI in privacy and security, making sure you understand the power and responsibility that come with AI. Next, it takes you through the complex world of information security and data privacy. It covers everything from the current state of security to how AI can detect threats and protect privacy. Additionally, it delves into ethical considerations to ensure the responsible use of AI in managing data privacy. Later chapters discuss strategies and future trends in using AI for data security, finding the right balance between security and privacy, and giving useful advice for organizations.
In the end, this book examines the current landscape and foresees the future, underscoring the vital importance of maintaining a balance between innovation and privacy in AI-powered security.
In the fast-changing cybersecurity landscape, enterprises are increasingly turning to artificial intelligence (AI) to strengthen their data security strategy. This section delves into the underlying concepts of AI-driven data security policies, studying how AI technologies play a critical role in strengthening organizations’ resilience against an ever-expanding array of cyber threats.
For many years, traditional data security methods depended on rule-based systems, firewalls, and signature-based intrusion detection systems. While effective to some extent, these tactics fail to keep up with the ever-changing nature of modern cyber threats. Organizations are recognizing the need for innovative, adaptive security solutions as the digital world grows more interconnected and intelligent.
The ability of AI to analyze massive volumes of data, discover trends, and respond in real time to emerging risks has had a transformative influence on data security. A subset of AI, machine learning algorithms, excels at detecting abnormalities and potential security concerns in large datasets. This dynamic capacity enables enterprises to move beyond static security measures and adopt a proactive and responsive approach to protecting critical data.
The ability to detect preemptive threats is one of the primary features of AI-driven data security strategies. Traditional approaches sometimes rely on predetermined rules, leaving them open to innovative and developing attack vectors. AI, on the other hand, learns from past data, always improving its grasp of normal and deviant behavior. This helps firms to detect anomalies that indicate cyberattacks, offering an early warning system that can mitigate any damage before it occurs.
What you will learn:
How AI is being used to detect and prevent cyberattacks in real-time What are the AI-powered techniques for anonymizing and de-identifying data, What are the latest advancements in AI-powered privacy-enhancing technologies (PETs) How to find the right balance between security and privacy
Who this book is for: Information security professionals, data scientists, and software developers seeking to gain an understanding of the latest trends and techniques in AI for information security.
1. Introduction 2. Understanding AI and Ethics 3. Information Security and Data Privacy Landscape 4. AI for Threat Detection and Prevention 5. Privacy-Preserving AI Techniques 6. Data Protection and Compliance 7. Securing AI Models 8. Case Studies 9. AI in Data Privacy and Ethics 10. AI and Data Security 11. Balance Between Security and Privacy 12. Best Practices and Recommendations 13. Future Trends and Challenges
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