An Abundance of April Data

You guys, you guys!! It is one week until our annual research report goes live. The data around API growth is enough to write about for months, but then we threw some AI on top of that and, well, it's just good stuff.

• Repatriation remains a trend, with SRE adoption a leading indicator of action.
• Organizations are becoming more digitally mature and eagerly embracing AI, but may stagnate without a supporting data strategy, which only 53% have defined.
• The automation use cases for generative AI are huge, with app delivery and security clearly two of the biggest beneficiaries of the technology.

There's more. So much more, but I wanted to share a few tidbits before I jump into my other data. Oh yes, there's more. This month I feel like the Billy Mays of data - but wait, there's more!

So don't change the channel, because you don't want to miss the data coming out of this month's pipeline.

Security Woes

One of the things we - that's the entire industry - is that while legitimate uses of AI are going to produce a bountiful harvest, the dark side of technology is also going to use AI and it's concerning.

So concerning that enterprises are blocking 18.5% of all AI/ML transactions according to a ZScaler ThreatLabz report. That's probably with good reason, as Stanford reports that according to the AI Incident Database, which tracks incidents related to the misuse of AI, 123 incidents were reported in 2023, a 32.3% increase from 2022. Now I know that 123 incidents seems small, but it's the growth that's concerning, because that represents a more than twentyfold increase in just ten years.

And enterprises are doing more than just blocking transactions, they're actively seeking to counter the obvious advantage AI gives attackers with their own experimentation. Our research is showing that the use of AI by attackers is a top - and I mean very tip top number one - concern. And Splunk found that seventy percent of CISOs believe AI gives the advantage to attackers over defenders and 35% are already experimenting with it for cyber defense, e.g., malware analysis, workflow automation and risk scoring. Those experiments include:

• 23% Creating detection rules
• 22% Workflow automation
• 20% Policy creation

That's not to say that AI isn't being used for other purposes. Indeed, AI is pervasive in every business function and IT domain right now. Consider this list of use cases from some AWS research :

• 43% Customer operations
• 40% Personal productivity
• 36% Coding support
• 32% Marketing and sales
• 14% Research & development

That first one - customer operations - includes chatbots. Which leads me to say that just as the mobile app madness led to us saying "there's an app for that", AI will lead to "there's a chatbot for that", as companies grab this low-hanging fruit and run with it. Really! Consider a finding from Snowflake : from May 2023 through January 2024 in the Streamlit community, chatbots went from 18% of LLM apps to 46%. And climbing.

But folks should note that consumers aren't as affectionate toward AI as perhaps we in the technology industry are. That Stanford research also found that in America, Pew data suggests that 52% of Americans report feeling more concerned than excited about AI, rising from 38% in 2022.

Time to consider carefully your strategy when it comes to customer service.

Besides, it turns out that most orgs are struggling with data quality and maturity, and all AI, but in particular generative AI, relies on good quality data.

Data Dragging Orgs Down

Yes, data is dragging orgs down. Concerns about data quality, data security, and data privacy (no, those two aren't the same, thanks for asking). That's not even taking into consideration the cost of compute to train an operate models at scale.

AWS CDO Agenda tell us the top AI challenge at 47% of respondents is ... data quality. Our own research tell us that in terms of digital maturity, i.e. readiness to harness AI, organizations are still struggling with data strategies. The problem is that data is coming from even more sources now, and I'm not talking about the corporate and customer stuff. I'm talking about telemetry and security data and the challenge of correlating activity with customers and employees. That's the holy grail, but doing that requires significant strategic and architectural changes to how the enterprise collects, stores, and processes data. The good news is that we found that 89% of organizations are working on it. They are making changes and thus they will be in a better position than the 11% that aren't.

And honestly, I believe the 8% of that know they aren't ready for AI more than the 3% that believe they are and thus need to make no changes. We'll check in next year and see how they're doing.

One good sign can be found in Snowflake's report , which notes the growing attentiveness to data security at a very granular level.

• The number of applied masking or row-access policies increased 98%.
• The number of columns with an assigned masking policy grew 97%.

That's the kind of thing that sets you up for success not just for AI but Zero Trust as well.

Dig in deeper

Generative AI - indeed, AI in general - is not going away. Like opening Pandora's box, releasing generative AI on the world has irrevocably changed the trajectory of our future.

It's not worth debating whether it's good or bad, because it's here and it's not going away.

So the best thing to do is sit down and strategize. AI is not a point solution nor a temporary trend. It's a strategic technology the likes of which we haven't seen in decades, and deserves careful consideration as to how best to incorporate into enterprise architectures because it's going to be there for a long, long time.

Until next month, take care!