FINAL WORD
Different AI applications require different models
Like the eventual reality that is multicloud , it is highly unlikely organisations will standardise on a single AI model . That is because different models can be a better fit for certain use cases .
It is important to note that AI is real and yes it is over-hyped .
That is why we are unsurprised to learn that the average enterprise is already using almost three distinct models , inclusive of open-source and proprietary models . When we look at the use of models based on use cases , we start to see a pattern .
For example , in use cases which rely heavily on sensitive corporate data or ideas , security ops and content creation , we see significant trends toward open-source models . On the other hand , looking at a use case for automation , we see Microsoft gaining use , largely due to its ability to integrate with the tools and processes already in use at many organisations .
This is important to understand because the practices , tools , and technologies needed to deliver and secure a SaaSmanaged AI model is different than that of a cloud-managed AI model is different than that of a self-managed AI model .
While there are certainly similarities , especially for security , there are significant differences that will need to be addressed for each deployment pattern used .
Analyse the use cases within your organisation and identify patterns in the adoption of different AI models . Consider factors such as data sensitivity , integration capabilities , and alignment with existing tools and processes . Tailor your approach to deployment and security based on the specific The core challenges remain the characteristics of each deployment pattern . same , and applying the same level of
There are a lot of considerations rigor to scaling and securing AI for building , operating , and securing AI applications will go a long way toward a applications , not the least of which is all the successful implementation . new requirements for model security and But forgoing attention to the differences scalability . But many of the lessons learned and leaping in without at least a semiformal strategy for addressing delivery from deploying modern applications across core , cloud , and edge for the past decade and security challenges is bound to lead to will serve organisations well . disappointment down the road . •
Best practices to build AI applications
• AI applications face many of the same challenges as any other modern application .
• The lessons you have learnt from scaling and securing existing modern applications will help you do the same for AI applications .
• Leverage existing knowledge and practices for application delivery and security .
• Expand to include approaches that recognise that different components of AI applications may have varying resource needs .
• Modern application deployments allow for flexibility in allocating resources based on the specific requirements .
• Given that many security solutions rely on behavioural analysis , including API security , means some adjustments will be necessary .
• You will need additional security capabilities to properly govern AI applications .
• Rethink traditional security approaches that may not adequately capture the nuances of conversational interactions .
• Explore innovative approaches such as real-time monitoring of interaction patterns and adaptive access control mechanisms .
• Recognise the critical role of APIs in facilitating communication with AI models .
• Invest in robust API security solutions to protect against unauthorised access , data breaches , malicious attacks .
• Analyse the use cases within your organisation and identify patterns in the adoption of different AI models .
• Consider factors such as data sensitivity , integration capabilities , and alignment with existing tools and processes .
• Tailor your approach to deployment and security based on the specific characteristics of each deployment pattern .
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