Your future of cybersecurity — the rules and algorithms of AI/ML
Here is a brief snap update on how the world, business, and your engineering of products are changing with artificial intelligence / machine learning, and cybersecurity globally. This is an exciting and new frontier, but we have already had experience in these areas. Applying those lessons; building on them, and adapting are gonna serve us very well.
Again .. rapid fire of a statement / question and the answer as of today.
What is working.. what is doin.. focus on the framework vs the results / output.
What are you looking for in these algorithms — originally fraud?
Then what, weaknesses, and finally appropriate use of data. Moral and ethical decisions issues exist and will expand too (especially by gov).
Privacy set precedent on rights of data. Next will be ai/ml humane decisions. Quantifying digitally human choices must reflect social norms as applied IRL.
How can we transition our technology and business operations to lead the charge?
To transition a business beyond monolithic to cloud, to agile, and ultimately ML/AI smart, it begins at fragmentation. Fragmenting / Atomizing of business and technology services is first step. Followed by demonstrated logic in scaling, troubleshooting, and self correction w/o humans. This is the path proven over the past 2 decades, and is available to all.
Where can leaders find guidance today — DevSecOps and agile are a mandatory first step. Lacking this skill will cost margin, jobs, and market relevance.
Expect regulations and market pressure on
— the algorithm, the machine language. The build out of these requires a deep connection with cyber and technology teams. Regulation will cover the logic and rules of these systems. To date, financial algorithms show early rules and requirements, but the scope and fear will rise as ai/ml directly engage with consumers.
About the author
- 24 Year technology veteran
- Helped launch clouds for EMC and Cisco