Zendata

Financial institutions are racing to build AI systems that can offer personalised advice, detect fraud and manage risk. But it's not as simple as plugging in some algorithms and watching the magic happen. These banks and insurers are grappling with a complex web of legacy systems, sensitive customer data and strict regulations. How do you build an AI that's smart enough to give tailored financial advice, but secure enough to protect your customers' life savings? The answer lies in a multi-layered AI architecture that prioritises privacy, compliance and seamless integration with existing systems. Our latest article examines this challenge using a real-world use case of an AI-powered personalised financial advisory system. At the core of this challenge is the intricate AI architecture in finance - a complex ecosystem that demands careful design and management for responsible innovation and risk mitigation. However, this is where many financial institutions struggle. This article explores the key components of AI architecture in finance, examining the implementation strategies, privacy considerations, and governance approaches that can support successful AI adoption in the financial sector. Read more: https://lnkd.in/eiab7nJw Or check out the key takeaways below. #dataprivacy #aigovernance #aiarchitecture #financialservices #aimodels

Governor Newsom's veto of SB 1047, California's landmark AI safety bill, signals a crucial moment in tech governance. While the bill aimed to set strict guardrails for AI development, its rejection highlights the complex balance between innovation and regulation. Key takeaways: - The veto emphasises the need for nuanced, risk-based approaches to AI oversight - It underscores concerns about stifling innovation in a rapidly evolving field - The decision keeps California competitive in the global AI race but raises questions about future safety measures This pause offers a chance to refine our approach to AI governance but it does beg the question: how can we craft regulations that protect public interest without hindering technological progress? Read more: https://lnkd.in/es9jvPwc #AIRegulation #TechPolicy #ResponsibleAI #CaliforniaTech

Deloitte's State of Generative AI Report 2024 says that the lack of a governance model was a top barrier to successful deployment for 29% of respondents. But that isn't necessarily stopping them from deploying them. As businesses implement these AI solutions, they must ask themselves: How can we harness the power of GenAI and Agent AI while effectively managing the associated risks? The answer lies in robust AI/Data Governance and privacy practices. In fact, 51% of respondents in the Deloitte survey recognized this, stating that establishing a governance framework for GenAI tools and applications was their main action to manage risks. At the heart of this challenge is the AI/Data Supply Chain - a complex ecosystem that requires careful management for responsible innovation and risk reduction. However, this is where most businesses struggle. This article explores the AI/Data Supply chain examining the risks, impacts and AI/Data Governance strategies that can support a successful implementation and adoption. Read more: https://lnkd.in/e68tjpnm Or check out the key takeaways below! #AIGovernance #DataPrivacy #ResponsibleAI #AIRiskManagement #AIInnovation

Gartner estimates that "By 2026, AI models from organisations that operationalise AI transparency, trust and security will achieve a 50% improvement in terms of adoption, business goals and user acceptance." AI adoption will continue to grow and this reinforces the need for strong, holistic governance frameworks. AI Trust, Risk and Security Management (TRiSM) is emerging as a leading approach for ensuring responsible AI use. This week's article explores the key components of AI TRiSM, common implementation challenges and how tools like Zendata can support companies in achieving their AI TRiSM objectives. Focusing on FinTech companies in particular, we examine how effective AI governance can lead to improved operational efficiency, enhanced risk management and secure AI implementation that supports business growth. Read more: https://lnkd.in/eEFSThYu Or check out the key takeaways below. #AIGovernance #AITRISM #dataprivacy #biasmitigation #aimodels

On the 30th of August 2024, the California Senate passed Assembly Bill 1008 (AB 1008) to amend how the CCPA defines personal information to include PII in various formats, including in artificial intelligence models. If AB 1008 is signed into law, it will have implications for data privacy and AI development/deployment by providing additional rights to consumers relating to how their data is used in AI models. This week's article investigates the impact of AB 1008 on businesses building or deploying AI models, the challenges they will face and how data privacy solutions like Zendata can help them overcome these challenges. We've included a high-level guide to mitigating AI privacy risks as well as commentary on how AB 1008 contrasts the Hamburg DPA's view that LLMs do not store personal data in a way that makes them subject to data protection laws. Read more: https://lnkd.in/ef_c6uwq Or check out the key takeaways from the article below. #dataprivacy #aigovernance #CCPA #aimodels #ab1008 #california #privacyrisks #riskmanagement

Did you know that 63% of people can be uniquely identified by just their gender, birth date and postcode? As businesses collect and analyse ever-increasing amounts of data, the risk of supposedly 'anonymous' information being traced back to individuals is increasing. The implications of data re-identification extend far beyond privacy concerns, touching on legal compliance, financial stability and brand reputation. This week's article examines how easy it is to re-identify data and the business implications. We explore real-world cases where data re-identification led to significant consequences and explore strategies that businesses could employ to mitigate the risks and turn privacy into a competitive advantage. Read the full article here: https://lnkd.in/eQ9Y6kMQ Or check out the key takeaways below. #dataprivacy #datagovernance #dataanoymisation #dataquality #datareidentification

Computer vision teaches computers to interpret and understand visual information. It's similar to how humans use their eyes and brains to understand the world around them. Our latest article, part of our AI governance series, examines the unique challenges of managing Computer Vision AI systems. We discuss how to create effective governance frameworks that address the specific needs of computer vision, including visual data management and ethical considerations. This guide offers practical insights on responsible implementation for businesses using or planning to use computer vision, from staff training to ongoing system monitoring. Read more: https://lnkd.in/exjXgAsR Or check out the key takeaways below. #AIgovernance #computervision #dataprivacy #AI #governance #deeplearning #facialrecognition

Deep Learning is designed to mimic the way the human brain processes information. The 'deep' in deep learning refers to the multiple hidden layers between the input and output layers. These hidden layers allow the network to handle complex, non-linear relationships in data, making it particularly effective for tasks involving unstructured data like images, text and audio. This article, part two of our series on the whether different AI systems have different governance requirements, will explore the fundamental concepts of deep learning, examine its applications across various industries and discuss the critical aspects of implementing effective governance frameworks. Read more: https://lnkd.in/eJrTmB8W #deeplearning #machinelearning #AI #AIgovernance

Are you going to BlackHat USA 2024? Join us for our briefing on Thursday 8th August at 2.30pm where our CEO, Narayana P., will be delivering a talk on Unmasking Privacy Risks In Alternative Ad-Tech Solutions! https://lnkd.in/ei_pqhcM #dataprivacy #AI #adtech #cookies #privacyrisk

Implementing an LLM isn't necessarily the most suitable option for most businesses given the resources required to build, train and operate it. This article, the first in a series on AI models and governance, explores Small Language Models (SLMs) as an alternative to Large Language Models (LLMs). We examine how SLMs can meet specific business needs and whether they require different governance approaches. We discuss: - Key differences between SLMs and LLMs - Specific use cases and their governance implications - Best practice strategies for SLM Governance Check out the full article here: https://lnkd.in/edTPntCR And the key takeaways below. #aigovernance #dataprivacy #datacuration #SLM #LLM #AIModel