Microsoft Power Platform: Security Risks and Solutions
November 14, 2024
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The use of Generative AI (GenAI) is on the rise as 67% will increase investment in this game-changer based upon a major impact. The survey: Deloitte’s report is The State of Generative AI in the Enterprise: Now Decides Next, and the firm spoke with 2,770 senior execs from 14 countries. The exception, for some companies GenAI “is the first proof that they can do something with AI.”
For businesses who are experiencing early benefits from GenAI, it is clear that the time for its application in practice has arrived. Significant challenges like issues with data quality, costs associated with the technology and its maintenance of infrastructure among others (Da Cruz et al., 2019; Marzoughi & Sarigol, 2020), sometimes non-suitable for all industries or by the consumption measurement being affected are still on this way until such regulatory changes as commented above happen. Deloitte’s lead for analytics, Jim Rowan noted that this is just the beginning of potential complications and challenges such as change management all leaders should be focusing on to ensure GenAI starts accruing value(propertyName = avoid link text).
GenAI has evolved beyond efficiency and cost savings for leaders, notes Costi Perricos, leader of GenAI at Deloitte Global. It increases innovation and enables businesses to improve products, services, customer relationships etc. These benefits are indicative of the transformational capabilities and versatility on offer with this technology.
While interest from senior leaders and board members around GenAI continues, an understandable erosion of excitement as new becomes normal. Many (63% of executives and 53% of boards) remain very involved, but these numbers are down from Q1 2024.
Although most GenAI projects are in pilot or testing stages — with 68% of firms pushing only 30%, at best, of experiments to full-scale implementation (Exhibit) the energizing effect is already evident. Data management has become a key priority, with 75% of businesses shelling out more on this part of the tech stack. However, there have been data challenges, which is why 55% of firms skipped some GenAI applications.
To cater for their data requirements (for GenAI) organisations are upping their data security by 54%, quality of the data marts by 48% and governance frameworks or new rules around corporate information policies by at least in place but with w/filtering systems by +45%).
While it is no doubt crucial to account for GenAI risks, three of the top four challenges regarding making successful deployments also relate to managing risk in some way: regulatory compliance (36%), handling risks (30%), and governance model not yet established (29%).
These concerns are arising from problems like model bias, hallucinations, privacy issues, trust and an overall need to defend against newer threats. According to Pi-Tech; companies are setting up governance structures for AI-related risks (51%), overseeing that they comply with regulations, such as GDPR and CCPA regularities compared with 49% expecting this level of regulation in June, conducting internal audits on GenAI tools.
While 41% cannot measure GenAI impact as organizations start moving beyond testing. Just 16% of respondents deliver frequent best practice reports that explain the value GenAI creates to their CFOs, for example. As GenAI applications continue to mature, the ability of results to be correctly quantified will play a large role in preserving buy-in from upper management.
Companies are also using performance metrics (48%), creating frameworks for assessing GenAI investments (38%) and monitoring changes in employee productivity to help prove value with the implementation of GenAI, according to respondents.
GenAI has reached a pivotal stage where organizations must balance high expectations with real-world challenges. Data quality, risk management, and regulatory compliance are critical to successfully implementing and scaling GenAI. As businesses continue to integrate GenAI into key processes, accurately measuring its impact will be crucial to securing long-term investment and support.
These are types of AI, which creates new content/solutions based on the existing data. This is critical as it allows for organizations to innovate, increase efficiency and improve products/services.
This provides to GenAI models with having well functional, high-quality data. If you pull in bad data, the results will be unreliable and some of GenAI strengths may not come into play.
The key challenges are, handling risks including regulatory compliance and data governance models as well as appraising how much real impact it makes.
The inherent technological risk with GenAI is being managed through the companies creating governance frameworks, regulatory compliance checks and internal audits.
In addition to increased efficiency and cost savings, GenAI is enabling organizations to design smarter solutions that can drive innovation for their products, improve customer relationships.
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