One Article Review
| Source |  AlienVault Lab Blog |
|---|---|
| Identifiant | 8456899 |
| Date de publication | 2024-02-29 11:00:00 (vue: 2024-02-29 11:08:54) |
| Titre | Gouvernance de l'IA et préservation de la vie privée AI governance and preserving privacy |
| Texte | AT&T Cybersecurity featured a dynamic cyber mashup panel with Akamai, Palo Alto Networks, SentinelOne, and the Cloud Security Alliance. We discussed some provocative topics around Artificial Intelligence (AI) and Machine Learning (ML) including responsible AI and securing AI. There were some good examples of best practices shared in an emerging AI world like implementing Zero Trust architecture and anonymization of sensitive data. Many thanks to our panelists for sharing their insights. Before diving into the hot topics around AI governance and protecting our privacy, let’s define ML and GenAI to provide some background on what they are and what they can do along with some real-world use case examples for better context on the impact and implications AI will have on our future. GenAI and ML Machine Learning (ML) is a subset of AI that relies on the development of algorithms to make decisions or predictions based on data without being explicitly programmed. It uses algorithms to automatically learn and improve from experience. GenAI is a subset of ML that focuses on creating new data samples that resemble real-world data. GenAI can produce new and original content through deep learning, a method in which data is processed like the human brain and is independent of direct human interaction. GenAI can produce new content based on text, images, 3D rendering, video, audio, music, and code and increasingly with multimodal capabilities can interpret different data prompts to generate different data types to describe an image, generate realistic images, create vibrant illustrations, predict contextually relevant content, answer questions in an informational way, and much more. Real world uses cases include summarizing reports, creating music in a specific style, develop and improve code faster, generate marketing content in different languages, detect and prevent fraud, optimize patient interactions, detect defects and quality issues, and predict and respond to cyber-attacks with automation capabilities at machine speed. Responsible AI Given the power to do good with AI - how do we balance the risk and reward for the good of society? What is an organization’s ethos and philosophy around AI governance? What is the organization’s philosophy around the reliability, transparency, accountability, safety, security, privacy, and fairness with AI, and one that is human-centered? It\'s important to build each of these pillarsn into an organization\'s AI innovation and business decision-making. Balancing the risk and reward of innovating AI/ML into an organization\'s ecosystem without compromising social responsibility and damaging the company\'s brand and reputation is crucial. At the center of AI where personal data is the DNA of our identity in a hyperconnected digital world, privacy is a top priority. Privacy concerns with AI In Cisco’s 2023 consumer privacy survey, a study of over 2600 consumers in 12 countries globally, indicates consumer awareness of data privacy rights is continuing to grow with the younger generations (age groups under 45) exercising their Data Subject Access rights and switching providers over their privacy practices and policies. Consumers support AI use but are also concerned. With those supporting AI for use: 48% believe AI can be useful in improving their lives 54% are willing to share anonymized personal data to improve AI products AI is an area that has some work to do to earn trust 60% of respondents believe the use of AI by organizations has already eroded trust in them 62% reported concerns about the business use of AI 72% of respondents indicated that having products and solutions aud |
| Envoyé | Oui |
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| Tags | Studies Prediction Cloud Technical |
| Stories | |
| Notes | ★★ |
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