Generative AI Security: Theories and Practices (Future of Business and Finance)

Price: $30.27
(as of Dec 24,2024 02:52:03 UTC – Details)

ASIN ‏ : ‎ B0CTLJ9BD4
Publisher ‏ : ‎ Springer (April 5, 2024)
Publication date ‏ : ‎ April 5, 2024
Language ‏ : ‎ English
File size ‏ : ‎ 10454 KB
Text-to-Speech ‏ : ‎ Enabled
Screen Reader ‏ : ‎ Supported
Enhanced typesetting ‏ : ‎ Enabled
X-Ray ‏ : ‎ Not Enabled
Word Wise ‏ : ‎ Enabled
Print length ‏ : ‎ 616 pages

Generative AI Security: Theories and Practices (Future of Business and Finance)

As businesses and financial institutions continue to adopt artificial intelligence (AI) technologies for various applications, the need for robust security measures to protect sensitive data and prevent potential cyber threats becomes increasingly crucial. One area of AI that is gaining traction in the realm of security is generative AI, which is used to create new data samples based on existing data.

Generative AI has the potential to revolutionize the way businesses and financial institutions operate by enabling them to generate new insights, automate decision-making processes, and enhance customer experiences. However, the use of generative AI also comes with its own set of challenges, particularly in terms of security and privacy.

In this post, we will explore some of the theories and practices surrounding generative AI security and discuss how businesses and financial institutions can effectively safeguard their data and systems in the age of AI.

Theories of Generative AI Security:

1. Adversarial attacks: One of the key challenges in generative AI security is the potential for adversarial attacks, where malicious actors can manipulate the AI model to generate fake data samples or disrupt the system’s performance. Businesses and financial institutions need to implement robust defenses, such as adversarial training and detection techniques, to mitigate the risks of such attacks.

2. Privacy preservation: Generative AI models have the capability to generate highly realistic data samples, raising concerns about the privacy of individuals and the potential misuse of sensitive information. To address these concerns, businesses and financial institutions must adhere to strict data protection regulations, implement anonymization techniques, and ensure transparent data practices.

Practices for Generative AI Security:

1. Secure model training: Businesses and financial institutions should ensure that their generative AI models are trained on secure and reliable data sources to minimize the risk of bias, errors, and vulnerabilities. Additionally, organizations should implement encryption techniques and access controls to protect the confidentiality and integrity of the training data.

2. Regular monitoring and auditing: To detect and respond to potential security threats in real-time, businesses and financial institutions should regularly monitor the performance of their generative AI models, conduct thorough security audits, and implement incident response plans. By proactively identifying and addressing security issues, organizations can enhance the resilience of their AI systems.

In conclusion, generative AI holds immense potential for transforming the future of business and finance, but it also poses significant security challenges that must be addressed. By adopting a proactive approach to generative AI security, businesses and financial institutions can leverage the benefits of AI technologies while safeguarding their data and systems from potential risks.
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