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While AI is revolutionising software development, it also introduces new security challenges. A recent ADAPT market trend report highlighted several significant risks associated with training generative AI models, such as the potential for biassed data and vulnerability to adversarial attacks. Additionally, issues like data privacy breaches and the integration of AI-generated code into existing systems pose further threats. If your organisation is embarking on an AI and ML development project , addressing these critical security considerations is crucial to safeguard your data, systems, and intellectual property (IP). After all, prevention is always better than cure. Taking the proper steps during the development process can help ensure that your AI models are both tamper-proof and future-proof. 1. Securing Data Pipelines Data security and lifecycle management are crucial for protecting the infrastructure that supports AI and machine learning initiatives. However, these aspects are...

Creating excellent user experiences involves providing clear feedback on system status, whether loading, deleting, completing a stage, or saving data. However, with AI prompts, a new dimension of feedback arises even before any content is generated. This feedback aims to improve AI interactions and assist users in crafting better prompts, creating a dynamic loop that continually assesses and improves prompt quality and output.  In traditional software, feedback typically confirms actions within a system’s predefined parameters. These systems are designed to complete specific tasks with set configurations, making their capabilities straightforward. However, AI introduces complexity as it relies on user prompts to derive meaning and generate responses. Prompt inputs can result in multiple unique outcomes, and responses can be partially or entirely inaccurate without the correct prompts to elicit the desired outcome. Thus, feedback mechanisms are fundamental to ensure accurate resul...

At the heart of any AI initiative lies data quality and preparation – its accuracy, relevance, and compliance. Without meticulous preparation, even the most sophisticated AI algorithms are destined to falter.  Today’s businesses harness data from an ever-expanding array of sources – from customer interactions to operational metrics and beyond – underscoring the complexity of modern data systems. Recent ADAPT research reveals that 66% of CIOs are unprepared to truly harness AI in 2024, and much of this comes down to data preparation and management. The challenge lies not just in aggregating this diverse data but in cleaning, assessing, standardising, and validating it. Moreover, data literacy is also critical. Employees at all levels must possess the skills to interpret, analyse, and derive insights from data effectively, ensuring that decision-makers across the organisation can harness the full potential of AI-driven insights. Achieving data readiness requires a comprehensive ap...

AI is a powerful technology that’s reshaping how our businesses operate and enhancing their products and services. According to McKinsey’s “The State of AI in Early 2024” report, global AI adoption across industries has reached 72%, highlighting its immense potential. For Australian organisations, it’s even higher; ADAPT research indicates that approximately 75% are either deploying or piloting AI and machine learning technologies. The vast array of AI business solutions, ranging from machine learning algorithms to natural language processing, offer significant benefits. They can improve accuracy and efficiency in decision-making processes, increase automation and productivity, enhance customer experiences, and provide data analytics and insights to inform business strategy. What’s more, as well as achieving their technological objectives, successful AI projects deliver substantial business value, contributing to improved operational efficiency, cost savings, revenue growth, and comp...

AI is rapidly changing how we do business and develop tech. It’s powerful, but it brings new security risks we can’t ignore. This post breaks down the complexities of AI systems, the threats they face, and how we can bake security into AI from the ground up. AI has seen a rapid advancement in terms of the technology and the abilities that it brings. Over the past two years generative AI has progressed at such a rate that most large corporations have struggled to keep up. Incorporating AI in their business processes, both internally and externally, has been on the rise, and we are seeing this trend increase every day.  With the boom that came from ChatGPT, we have also seen new attacks targeting AI technology. From prompt injections to model poisoning, we are seeing new threats and vulnerabilities that exist only for AI services and applications. Although these attacks are new in their nature, the underlying security concepts we have today are still relevant, where protecting dat...