Breakneck speeds in worldwide AI adoption require equally breakneck rates of mindfulness around Trust, Risk and Security Management protocols

Firms looking to incorporate AI pervasively need to be aware of what Gartner dubs TRiSM: trust, risk and security management systems and protocols.

As the world has seen, AI opens up many new opportunities for organizations – especially when combined with public cloud solutions.

However, AI is not without its risks. In spite of the widespread adoption, many businesses have not increased their security posture to keep up. This is a problem: according to a recent Gartner survey, 41% of respondents reported a security breach or privacy incident related to AI.


Dave Sasson, Chief Strategy Officer, Hanu

Here are other AI, cloud and data coupling trends that involve a TriSM perspective worth noting:

    • Making smart buildings smarter
      As the construction and operation of commercial buildings is estimated to account for 38% of worldwide carbon dioxide emissions, smart buildings will need to be more ‘self-managing’ by being trained using digital twins before deployment.

    • Leveraging personal health data in the Cloud
      With trust being at the core of data coupling, digitalized healthcare sectors will be able to blend population and patient data for AI development and analytics, query imaging metadata with clinical data, and use patient engagement tools for more personalized treatment and engagement. This lets clinician researchers and personnel to perform everything from AI development to deep analytics, data visualization, operational management and compliance with all industry regulations such as ISO, GDPR, HITRUST CSF, and HIPAA through BAA coverage; the 21st Century Cures Act; and CMS Interoperability and Patient Access final rules.

    • Democratizing supercomputer power for AI
      For organizations looking to scale their in-house AI solutions to the Cloud, firms such as Microsoft and Nvidia are leading the field by partnering to build more supercomputers optimized for accelerated AI computation to enable scalable deep learning and AI training. Use cases include training language models of unprecedented sizes, building generative AI systems (with ChatGPT grabbing widespread attention), and creating complex recommender systems at scale.

    • Leveraging AI for predictive supply chain management
      With long-term supply chain disruptions plaguing the world for years now, cloud-based AI solutions can offer predictive, real-time analytics to minimize challenges. Predictive order management allows businesses to aggregate omnichannel inventory data and rule-based systems to automate fulfillment. Such systems can drill deep into customer and inventory data to predict future order volumes and potential hurdles —in real-time, with up-to-the minute visibility into order and fulfillment processes. Predictive supply chain management also offer dashboards for supply and demand insights to allow businesses to predict shortages and supply constraints, and to run simulations and predict (and avoid) over-stocking, missed orders, and stock outages.

    • Innovating with self-correcting and intelligent automation As generative AI evolves in sophistication, businesses will be able to delegate even more menial tasks to machine learning algorithms. These include repetitive tasks like data analysis and the monitoring of critical-but-tedious workflows. With time-consuming and repetitive tasks out of the way, business teams will be able to concentrate on innovation and strategy.

Finally, as cloud accessibility and scalability increases exponentially, AI solutions that were once limited to a handful of SaaS offerings will become more abundant and economical, making the TRiSM mindset even more critical for organizations to operate by.