AI has revolutionized how we do business by improving efficiency and productivity. One example of impact in the fashion industry is the use of AI fashion models. These virtual models allow for model diversity and increased efficiency for marketing campaigns. IT staff tasked with AI can help modeling agencies to gain these benefits.
As we become more connected digitally, there is an increased need for the protection and responsible use of data. C-level IT executives can make digital trust a key focus to create a secure and reliable digital ecosystem and foster positive relationships with customers and stakeholders.
Generative Adversarial Networks (GANs) can create realistic fake IDs that bypass weak AI KYC checks. This gives bad actors access to systems and can lead to an increase in fraud and money laundering. Information security officers must be aware of this risk and the solutions to detect AI generated IDs.
Hybrid work models have allowed businesses to transition to the future of work. Booking of rooms and desks are difficult with these models when there is limited space. IT can tackle these difficulties by implementing hybrid work management software to allow for the efficient use of resources and boost productivity.
Quantum computers have been an industry buzzword for quite some time. However, this revolutionary advancement in computing is quickly becoming a reality. Once here, these computers would have dire effects on current application security. Technology leaders should understand exactly how quantum computers would affect them and start taking proactive measures to mitigate their impact on their infrastructure and data security.
The Payment Card Industry Data Security Standard (PCI DSS) has made significant changes to its requirements for safeguarding cardholder information. These changes represent a shift that increases the security of cardholder data within this ever-evolving threat landscape and provides more flexibility for organizations. Security leaders striving for PCI Compliance must ensure their teams are well-informed about the updates in this new release and understand its impact on their organization's payment security protocols and compliance obligations to maintain or achieve compliance.
Small in-house IT teams can find it difficult to manage the software development process as business demands grow. This leads to a software feature backlogs that hinder business operations. No-code and low-code approaches can boost application development and reduce project timelines by providing quick feedback and modification of prototypes.
Large language models (LLMs) have disrupted many industries and pushed businesses, including small and medium-sized enterprises (SMEs), to attempt AI application implementations. LLMs are fine-tuned on business data to handle a specific domain, but this process is too costly and resource intensive for SMEs. AI engineers can replace fine-tuning with a vector database, which acts as long-term memory and allows an LLM to use up-to-date business data.
Testing code is crucial for software reliability, which can be ensured by meeting code coverage targets. Meta's TestGen-LLM, an advanced language model, improves test generation and coverage, enhancing software quality. Software Quality Assurance managers should add LLMs like TestGen-LLM to the QA process to boost test quality, efficiency, and software reliability.
AI models require large amounts of data during the training phase, otherwise, models will be biased and perform poorly on given tasks. Although vendors sell datasets to remedy this, datasets are not available for all domains. IT teams tasked with AI responsibilities can use synthetic data generation to train AI models for data-scarce domains to create high-quality datasets on the cheap and fast-track AI model deployment.