AI lifecycle management

“AI regulation: It's not an abstract possibility, it's a concrete reality,” said Kay Firth-Butterfield, CEO of Good Tech Advisory and the world's foremost AI ethicist in our recent webinar on navigating the evolving AI regulatory landscape. Alongside Kay and Shyam Nagarajan, Executive Partner at IBM Consulting, I had the privilege of discussing how governance tools can address these emerging compliance challenges. As businesses navigate the risks and rewards of AI, the stakes are rising. From negative publicity to lawsuits and hefty fines, it’s clear that robust governance frameworks are no longer just value-adds but essential components of the larger AI stack. “When we're thinking about governance,” Firth-Butterfield shared, “we really need to think about multi-party access and an independent certification.” These capabilities create the transparency required to establish trust in AI training and usage, she said. Our conversation also focused on our recently-introduced Prove AI tool, an AI governance solution that enhances transparency, access, and auditability for AI datasets, from training and fine-tuning data to inputs and outputs. By creating an auditable and tamper-proof record of AI data on the Hedera blockchain, Prove AI can be augment AI governance frameworks—-specifically IBM watsonx.governance—and enable organizations to create truly trustworthy AI. Growing maturity in AI governance and regulation Though it may still be early days in the regulatory environment, the complexity of AI regulation is rapidly increasing as AI becomes more integrated into daily life and business. The EU AI Act, often referred to as AI's GDPR moment, is a significant milestone, setting comprehensive standards for AI deployment within the EU. Similarly, other regions are advancing their regulatory frameworks. In the US, various states are taking action at different rates, with Colorado’s AI Act leading the way. In the absence of a global regulatory consensus on AI, businesses face both challenges and opportunities. While the EU AI Act provides a detailed blueprint, regions like China have enacted a myriad of specific regulations targeting different aspects of AI, from algorithmic recommendations to data security. Understanding these diverse regulatory environments is crucial for all businesses operating on a global scale. Yet with near-constant new developments, often tailored down to specific use cases, it can be hard to keep up. It is increasingly clear that businesses need comprehensive, reliable AI governance tools and partnerships to ensure their technology stays abreast of regulatory measures. Understanding risk in AI governance As AI models become integral to organizational operations, responsible AI is increasingly critical. These models are embedded across diverse platforms, from cloud services like Azure and AWS to on-premise systems and tools like Zoom and Outlook. This ubiquity makes risk management both essential and complex. Currently, businesses face three main types of risk when using AI tools: reputational risk, organizational risk, and regulatory risk. The most pressing and dynamic category is regulatory risk. The fragmented approach to Ai regulation exposes businesses to noncompliance and hefty fines. Amid this shifting landscape, AI governance solutions cannot be one-size-fits-all, as each organization’s requirements vary based on industry, geography, and specific use cases. Reputational risk is straightforward: AI errors can severely damage an organization’s repudiation, associating it with prejudiced perspectives. Firth-Butterfield highlighted the critical issue of bias in AI systems, which poses significant ethical and practical challenges, particularly in applications like hiring algorithms and financial services. Addressing these biases requires a concerted effort to diversify the data used in AI training and implement rigorous fairness and accountability measures. “Every organization has always needed an AI governance strategy, but none so much as now,” Firth-Butterfield emphasized. To be effective, Nagarajan added, a governance solution must be adaptable, enable risk assessments, and support management of governance workflows across multiple stakeholders. It should capture model metadata for auditability and transparency and monitor metrics such as accuracy, drift, bias, fairness, and discrimination. Moreover, it must be usable across the entire organization. From principles to practice in responsible AI Establishing a framework for the safe and ethical development and use of Ai is crucial for organizations to ensure adversarial robustness: the confidence that AI systems are resilient against tampering and manipulation. Similar to financial audits, this practice verifies not just the integrity of Ai models but also their inputs, ensuring that no bad actors have compromised any part of the platform, thereby laying the groundwork for trust. As I discussed in the webinar, Prove AI has partnered with IBM to launch Prove AI, addressing the current regulatory environment and propelling AI innovation forward safely and securely. Prove AI leverages blockchain technology to ensure tamper-proof records of data quality, integrating seamlessly with existing governance frameworks. This tool not only meets current regulatory requirements but also prepares businesses for future compliance challenges as Ai regulations evolve, thanks to its flexible and transparent nature. Meanwhile, watsonx.governance provides an integrated framework to manage the end-to-end lifecycle of AI models, supporting multi-stakeholder collaboration and ensuring compliance and risk management. Prove AI extends watsonx’s capabilities by introducing tamper-proof, auditable version control, and multi-stakeholder attestation for data quality—crucial for enterprise-level AI model deployment yet flexible enough for a range of use cases. As Nagarajan shared, “Your governance solution needs to adjust to your specific situation.” The future of AI governance As AI continues to permeate various sectors, businesses must prioritize transparency, fairness, and accountability in their AI systems. Only comprehensive, adaptable, and secure governance solutions can enable organizations to harness AI's full potential while maintaining trust and accountability. By adopting comprehensive governance solutions like Prove AI, organizations can not only comply with current regulations but also build a foundation of trust and reliability for the future. As we discussed, effective AI governance does more than ensure compliance and mitigate risks: it accelerates innovation. By providing robust governance frameworks, companies can confidently develop and deploy AI technologies, knowing they can quickly address issues as they arise. 3 Key takeaways AI governance is now a business necessity, not a luxury. AI regulations are already being actively enforced, and more regulations are coming. To achieve AI trust, organizations need to start with ethically-sourced, secure training data. You can view the full webinar here.

TRANSCRIPT 00:09.920 --> 00:14.056 Thank you for coming today. Appreciate it. Hope this is going to be entertaining. 00:14.168 --> 00:17.640 This presentation is going to be pretty high level, right, 00:17.680 --> 00:21.336 about strategy, right? So as we just heard, things are early. 00:21.408 --> 00:24.856 It's early for compliance, it's early for regulation and 00:24.928 --> 00:28.540 it's still very early for, you know, what the right approach is 00:28.580 --> 00:31.244 going to be for managing, you know, 00:31.332 --> 00:36.524 any, anything related to data compliance when you create LLMs 00:36.572 --> 00:40.252 or anything adjacent to it. So. Right. I think that it 00:40.276 --> 00:43.228 would, I think it's the right time to step back and sort of think, 00:43.284 --> 00:46.588 well, like if we're going to develop LLMs at scale, 00:46.684 --> 00:49.900 right, if we're really going to start developing, you know, 00:49.940 --> 00:53.644 not just one or two, but have teams, 50 teams, 100 teams, 00:53.692 --> 00:56.652 each of them building on top of gen AI models, 00:56.796 --> 01:00.664 right. What do we think is really going to with the data management, 01:00.752 --> 01:04.360 with the compliance types of stuff and what can we do now to 01:04.400 --> 01:08.232 prepare us for this inevitable headache? Right, so let's be clear, it's going to 01:08.256 --> 01:11.960 be a headache, right? Anytime a new computing platform comes 01:12.000 --> 01:15.720 along, it's been a headache, right? So we can go through 01:15.760 --> 01:19.540 examples here and we're already seeing some headaches here 01:20.080 --> 01:23.624 in the headlines, right? So specific 01:23.672 --> 01:27.512 things that are unique and emergent with LLMs, right, are just some 01:27.536 --> 01:30.872 things listed here. Very small, undetectable bugs that lead 01:30.896 --> 01:35.124 to a very high blast radius. Legal and compliance 01:35.172 --> 01:38.308 issues, very high amounts of liability, 01:38.484 --> 01:42.404 need clarity on who's actually liable for what when multiple parties 01:42.452 --> 01:45.604 are involved. And just the general how do we develop 01:45.692 --> 01:49.668 quickly without having to have a meeting every 01:49.724 --> 01:53.252 week about are we following the rules, are we doing the right thing? These are 01:53.276 --> 01:56.852 things that are in our way as AI developers and if we 01:56.876 --> 01:59.918 don't sort of just step back and think about, well, what can we do today 01:59.974 --> 02:03.582 to prevent us from getting sidelined later? Right? You think that that's the 02:03.606 --> 02:06.942 right activity and that's where we should 02:06.966 --> 02:10.542 be investing time in today. Right. So I'll make the case that AI development is 02:10.566 --> 02:13.918 very different than traditional development. That's okay. 02:14.014 --> 02:18.490 This has happened several times over the last couple of decades. Web 2.0, 02:19.030 --> 02:22.542 all of this every time a new computing paradigm has come 02:22.566 --> 02:26.610 along, right. We've had to adapt. Right. And my thesis here is 02:26.650 --> 02:29.842 that every time we've needed to adapt, there's been an attempt 02:29.906 --> 02:33.410 to lock things down. Right? We thought that that was the right thing to 02:33.450 --> 02:37.470 So let's go Back to Web 2.0. Imagine it's 1998. 02:38.330 --> 02:41.730 Imagine, you know, oh my gosh, we're going to release software. How many Times. 02:41.770 --> 02:45.410 We're going to release once a month. That's crazy, right? We can't. How could 02:45.450 --> 02:48.578 we possibly release software once a month? Right? This sounds like, 02:48.634 --> 02:52.184 like nuts. What about the compliance? What about the rules? 02:52.232 --> 02:56.008 What about all of these things that we need to follow? Right. I remember doing 02:56.064 --> 02:59.512 this, I worked for a big box retailer. The solution 02:59.576 --> 03:02.264 was, you know what we need to do? We need to fill out a paper 03:02.312 --> 03:05.208 form and we need to fax it and then we need to get a sign 03:05.264 --> 03:08.340 off from a VP and that's how we're going to do a release. 03:08.640 --> 03:11.992 Yep. And then what happened after that? Right, what happened after that was, well, 03:12.016 --> 03:15.864 actually this is a pain in the neck. We can't release software with these encumbrances. 03:15.992 --> 03:19.080 This is too much. So you know what we need? We need to OCR these 03:19.120 --> 03:22.606 forms. That's what we should be buying. We should totally 03:22.638 --> 03:27.166 digitize these forms and we can just process 03:27.318 --> 03:30.654 signature forms at scale. That sounds like a great idea. 03:30.782 --> 03:34.062 Was it a great idea? No, it was not a great idea. That's not 03:34.086 --> 03:37.518 what happened. That's not the world we live in today where we can do thousands 03:37.534 --> 03:41.582 of releases per week, small ones, albeit with pipelines. 03:41.726 --> 03:45.262 It's just not what happened and it's never been what happened every 03:45.286 --> 03:48.340 time a new paradigm comes along. 03:48.960 --> 03:52.504 So I always think that it's, hey, if that's what happened with web 2.0. 03:52.592 --> 03:55.752 And then we had cloud, right? We had the cloud revolution and we said, 03:55.776 --> 03:59.352 all right, we need to make very small incremental updates and 03:59.456 --> 04:02.920 we need something like what we've landed on is not, 04:02.960 --> 04:05.640 hey, you know what we need to do? We need to do security reviews once 04:05.680 --> 04:09.064 a day, right? That was a knee jerk reaction and said, how are 04:09.072 --> 04:12.248 we going to make so many releases without enough 04:12.304 --> 04:15.512 compliance and reviews in place? And in the beginning it 04:15.536 --> 04:18.296 was, hey, we just need to do more reviews, we need to do more compliance 04:18.328 --> 04:21.828 reviews, we need to embed security professionals within 04:21.884 --> 04:24.340 your team and they need to look at stuff once a day. Right. Again, 04:24.380 --> 04:27.844 is that the world we live in today? No, it's not. Right? 04:27.932 --> 04:31.652 We've automated those things and we've built those into our development pipelines, 04:31.796 --> 04:35.124 which required a fundamentally different approach to releasing software. 04:35.252 --> 04:38.820 In that case, it was pipelines, it was CICD pipelines and 04:38.940 --> 04:42.468 we moved on with ourselves. And now we live in a world where 04:42.604 --> 04:46.002 we can release as developers. We can release. So I'd make the 04:46.026 --> 04:49.666 case that for AI development, we have yet another 04:49.738 --> 04:53.714 problem here, a very simplified version of it, is that you 04:53.722 --> 04:56.834 have more moving pieces than we previously had. 04:57.002 --> 05:00.082 In addition to the actual software, the LLM, we have the 05:00.106 --> 05:03.602 training data, we have possibly RAG data. We might 05:03.626 --> 05:07.474 have other data that we need to power these applications. We have context, 05:07.522 --> 05:11.362 we have the configuration and there's probably a few other sources, 05:11.506 --> 05:15.244 each of which are living currently in little silos 05:15.292 --> 05:18.492 typically. Right. So one person might own some of this. One person might 05:18.516 --> 05:21.560 own some of this. You might have to ask somebody for this training set. 05:22.580 --> 05:26.220 This does not look like normal software development. Right. What that means 05:26.260 --> 05:29.836 is we can expect we're going to need to change how we do software development 05:29.908 --> 05:33.004 again. The world just gets a lot more complicated. The development landscape 05:33.052 --> 05:35.740 gets more complicated. Okay, so, 05:35.860 --> 05:39.004 right, so I'm here representing 05:39.132 --> 05:43.388 our product. I'm the CTO for PROVE AI. Right. This is our philosophy. 05:43.484 --> 05:47.454 It's designed to help organizations de risk AI 05:47.502 --> 05:51.006 simplify compliance and unify governance. So the idea is 05:51.158 --> 05:54.318 we don't really know what's going to happen in the future. We don't know what's 05:54.334 --> 05:58.126 going to happen with compliance. But what we can be sure of now 05:58.278 --> 06:01.502 is that it's going to be the 06:01.526 --> 06:04.958 thing that you can do now is get your data stored in the right place 06:05.094 --> 06:08.414 in a way that will set you up for success with later and most importantly, 06:08.462 --> 06:11.742 not get in the way of your development teams. That's something you can 06:11.766 --> 06:15.272 act on today. That really you're not wasting 06:15.336 --> 06:17.816 anything. You're going to have to do this anyway. You're going to have to store 06:17.848 --> 06:21.512 your observability data somewhere, your compliance data somewhere. Why not think 06:21.536 --> 06:24.020 this through now about the use cases of the future. 06:24.400 --> 06:27.608 Get this data written to the right type of place. Get your event streams written 06:27.624 --> 06:30.856 to the right place so you don't have to waste time later redoing 06:30.888 --> 06:34.184 everything. That is what our product 06:34.272 --> 06:37.848 provides. We are in early access. As you can imagine, 06:37.944 --> 06:41.288 this is an emergent type of use case. We're not embarrassed 06:41.304 --> 06:45.052 by that. It's early, that's fine. And we 06:45.076 --> 06:48.844 just think that it's worth the investment now to do that. We do use a 06:48.852 --> 06:52.428 decentralized ledger as the source of record behind. 06:52.564 --> 06:56.156 So what is this magic data store I'm talking about for PROVE AI? 06:56.188 --> 06:59.788 It's a decentralized ledger. There's a number of reasons. We think that 06:59.844 --> 07:03.980 there's a very legitimate set of use cases that 07:04.020 --> 07:07.692 are very well suited towards a distributed 07:07.836 --> 07:11.388 ledger. Azure data store for these types of compliance related events. 07:11.484 --> 07:14.860 Even if it's not the actual compliance data, it might be hashes of that 07:14.900 --> 07:18.172 data. Something that allows you to prove authenticity 07:18.316 --> 07:21.692 have it be tamper proof and allow you to do all of 07:21.716 --> 07:25.292 these use cases which I'll go through right now that 07:25.316 --> 07:28.780 we expect to be important in the future. All right, so now 07:28.900 --> 07:32.220 again, what I promised today was we'll go through a few 07:32.340 --> 07:35.612 forward looking use cases. These are food for thought. These are like, 07:35.716 --> 07:38.716 yeah, will you have to do this organization? 07:38.828 --> 07:42.472 We think so. I think so. And say, 07:42.656 --> 07:48.104 if I'm really going to need to solve these types of problems, then maybe 07:48.152 --> 07:51.640 adopting something that's ledger based is actually a good idea. 07:51.720 --> 07:54.792 So I'll go through a few sort of 07:54.816 --> 07:58.232 tenets or use cases that we think are going to be important. 07:58.416 --> 08:01.560 So one is this notion of being always compliant and 08:01.600 --> 08:05.816 observable for AI and any sort of gen AI 08:05.928 --> 08:10.154 sort of activity here. Collect everything, observe everything, 08:10.312 --> 08:13.718 right? Put it in one place. Most important to this use case 08:13.774 --> 08:17.798 is like, let's assume that we're not going to only have one MLOps tool. 08:17.854 --> 08:21.478 Let's assume you're going to do AI development at scale. You're going to have 08:21.534 --> 08:24.662 50 teams, even 10 teams. They're probably not 08:24.686 --> 08:28.502 going to want to use all of the same tooling, just like history has 08:28.526 --> 08:32.166 told us, right? So we're all going to standardize on web application container 08:32.198 --> 08:35.782 No. Has that ever happened? It's never happened. Right. So should 08:35.806 --> 08:39.130 we expect it to happen with AI development? No, we shouldn't. 08:39.290 --> 08:42.650 So we can prepare for this inevitability now. And then we say, 08:42.690 --> 08:46.522 okay, if we expect our teams to use the tools that they 08:46.546 --> 08:50.042 want to use to be successful, we can therefore assume that 08:50.066 --> 08:52.910 there's probably going to be multiple MLOps tools in the mix. 08:54.210 --> 08:58.042 Let's also assume now we need to be always compliant. So this 08:58.066 --> 09:01.722 is not a notion of quarterly review is good enough, weekly review is good 09:01.746 --> 09:04.970 enough. Things are going to change so quickly and each little 09:05.010 --> 09:08.906 change may have a big impact on whatever happens compliance wise. 09:09.018 --> 09:12.410 So I should basically react to that by saying my 09:12.450 --> 09:15.850 goal as a business, as a cto, as a cio, is I want to be 09:15.890 --> 09:19.706 always compliant. I want to basically say everything that's 09:19.738 --> 09:23.402 logged is already logged, it's transparent. I don't need to make a 09:23.426 --> 09:26.618 phone call, I don't need to upload artifacts because that's 09:26.634 --> 09:30.026 going to be too slow, right? I don't have time every time somebody 09:30.058 --> 09:33.498 wants to push a new model version to go upload a bunch of artifacts. 09:33.514 --> 09:36.712 That proves that I'm doing the right thing with my data. Right? 09:36.736 --> 09:40.488 And again, you don't have to wait for compliance to, to, to move on this, 09:40.544 --> 09:44.040 right? As, as technology professionals, our job is to do the right thing, 09:44.080 --> 09:46.840 right? It's not to comply with the law, it's to do the right thing. 09:46.880 --> 09:50.232 Right? Do the right thing for our customers, do the right thing for, you know, 09:50.256 --> 09:53.704 our business. So, right. There's no reason to wait for compliance 09:53.752 --> 09:57.192 in this case, right? We, it's the right thing to do is to say, 09:57.216 --> 10:00.440 yeah, this is going to be too slow. Why don't I get everything written to 10:00.480 --> 10:03.858 a place that's transparent, observable and provably correct, 10:03.954 --> 10:07.506 provably accurate, such I don't have to field requests 10:07.538 --> 10:11.042 for information, I don't have to upload stuff, and I don't have to keep 10:11.146 --> 10:14.390 being bombarded with additional requests for stuff. 10:14.970 --> 10:19.314 Okay, so observable 10:19.362 --> 10:22.530 and compliant, I think are the two coins that we're talking about here. 10:22.570 --> 10:25.842 But the idea is the game is not going 10:25.866 --> 10:29.058 to be about looking at stuff once a month and 10:29.114 --> 10:33.266 having a review. It's going to be about it being available and online 10:33.338 --> 10:36.974 at all times. Completely hands off. Right? So you, as a technology 10:37.062 --> 10:40.702 professional, you don't want to get involved, right? Somebody says, are you guys compliant 10:40.766 --> 10:43.870 or are you guys doing the right thing with model bias and drift? 10:44.030 --> 10:47.422 You say, I show my work, I put all of our work here. 10:47.526 --> 10:51.150 It's observable if you want to go look at it. This is why we make 10:51.190 --> 10:54.814 all of our information available. Maybe internally, maybe externally. 10:54.862 --> 10:58.670 It doesn't matter. The point is it shouldn't fall on additional work for us as 10:58.710 --> 11:02.472 technologists. All right, so I like 11:02.496 --> 11:06.248 to just talk about a very real use case here. A use case could be. 11:06.304 --> 11:10.020 Let's say I'm a heavy equipment manufacturer. Let's say I make 11:10.400 --> 11:13.752 some sort of robotic devices that do some sort of 11:13.776 --> 11:17.480 industrial application. And let's say that these devices have 11:17.520 --> 11:21.896 embedded firmware now that have some sort of AI model built 11:21.928 --> 11:25.288 into it. Typical. Like you can imagine all sorts 11:25.304 --> 11:28.020 of X ray machines with gen AI models at this point. 11:28.480 --> 11:32.056 Not a far fetched use case. Industrial is something we've 11:32.088 --> 11:35.992 seen interest in. What happens now though? Let's say 11:36.016 --> 11:39.512 you have a product line of 50 different teams now trying to 11:39.536 --> 11:42.984 build AI into their products. And let's say that now 11:43.072 --> 11:46.552 the maintenance requirements on these things change in 11:46.576 --> 11:49.736 reaction to your exact models. All right. As a developer, 11:49.768 --> 11:53.480 I'm not going to have time to update my maintenance procedures every 11:53.520 --> 11:56.392 time I deploy tweaks to the model. I'm not going to have time to go 11:56.416 --> 11:59.824 through a compliance and safety review. The idea 11:59.872 --> 12:02.544 here is that I just want to automate all of this every time I make 12:02.552 --> 12:06.512 a change, possibly with an AI copilot of sorts, I would 12:06.536 --> 12:09.700 like to have an AI, maybe just generate documentation and maintenance 12:10.040 --> 12:13.740 stuff for me if that's the vision of what I want to do. Well then 12:14.040 --> 12:17.680 what happens when your automated documentation says something 12:17.720 --> 12:21.860 terribly wrong about a maintenance procedure? Massive liability, 12:22.280 --> 12:25.744 massive risk of public safety. How do you get around this 12:25.912 --> 12:29.222 without having to have really deep reviews every time you 12:29.246 --> 12:32.822 make a single change? Right. We can't live in that world. So what you 12:32.846 --> 12:36.102 can do though, right, in this case is just get into the notion of, 12:36.126 --> 12:39.158 look, I show my work, we're just going to keep developing, we're going to keep 12:39.214 --> 12:42.806 incrementally updating stuff. I need a way to share this information with 12:42.878 --> 12:46.054 third parties, my customers, about exactly what have I done 12:46.142 --> 12:50.214 to these models, what exactly is the impact? So collectively 12:50.262 --> 12:53.500 we can make a decision about where liability sits with, 12:53.590 --> 12:56.528 with these sort of automated maintenance stuff, right? 12:56.584 --> 13:00.608 And have a very clear sense of roles and responsibilities 13:00.704 --> 13:03.740 to avoid safety issues and legal issues. 13:04.200 --> 13:07.680 So lots of 13:07.800 --> 13:11.152 complicated things here. So next I wanted to 13:11.176 --> 13:14.820 talk about use cases, secure third party verifiability. 13:16.040 --> 13:19.776 So when we talk about this, right, we say compliance 13:19.808 --> 13:23.328 and safety, right? We typically talk about it as internal, 13:23.424 --> 13:26.896 right? But now let's think about the use case where, 13:26.968 --> 13:30.528 you know, I'm a software vendor today and I want to see your SOC 13:30.584 --> 13:34.064 2 compliance, right? Very typical type of thing. 13:34.232 --> 13:37.180 Maybe I develop software in the web 2.0 world. 13:38.040 --> 13:42.180 I want to go and embed a vendor's piece of software into my solution. 13:42.520 --> 13:46.112 Typical thing to do, show me your compliance, I want to see your certificates. 13:46.176 --> 13:49.618 Because I can only do business with other organizations that have high 13:49.674 --> 13:52.738 quality, you know, high degree of compliance, 13:52.834 --> 13:56.546 right? Normal. Okay, now what happens in the 13:56.618 --> 14:00.306 LLM world, right? What happens when I'm trying to license LLMs? What happens 14:00.338 --> 14:03.538 when I'm trying to share this type of information with third 14:03.594 --> 14:06.950 parties? When there's noticeably more moving parts, 14:07.450 --> 14:10.610 it gets more complicated. Right? Again, the show your 14:10.650 --> 14:13.530 work aspect here is probably the best solution, 14:13.650 --> 14:17.002 right? Instead of me saying I'll give you a certificate and we'll do an 14:17.026 --> 14:20.794 audit yearly. It's probably going to be, I'm just going to 14:20.802 --> 14:23.642 give you an event stream, I'm going to write an event stream of what we 14:23.666 --> 14:27.242 do, who did it and how it happens, right? I'm going 14:27.266 --> 14:30.762 to show that I'm always compliant and I'm going to share this 14:30.786 --> 14:34.042 information in a secure way with you that doesn't require us to even 14:34.066 --> 14:37.274 have a phone call or an email exchange. Right? So if 14:37.282 --> 14:40.602 you believe that that's sort of the personally, we believe that 14:40.626 --> 14:43.702 that's sort of the inevitable, you know, way that this nets out. And in this 14:43.726 --> 14:47.622 case writing this type of information to a decentralized ledger makes, makes a whole lot 14:47.646 --> 14:50.694 of sense. You know, example could be in healthcare, 14:50.742 --> 14:54.278 right? So let's say I have a, you know, let's say I buy a provider 14:54.294 --> 14:58.010 of medical chatbots that provide some sort of medical advice to patients, 14:58.430 --> 15:01.622 you know, and you can imagine there's all sorts of 15:01.646 --> 15:04.982 liability issues there. So let's say I say, hey, I expect you 15:05.006 --> 15:08.552 as an LLM provider, as a chatbot provider to be continually 15:08.616 --> 15:12.152 updating and improving the quality of your service. On the other hand, 15:12.336 --> 15:15.768 we can't have a two month gap where you fell out of compliance of 15:15.824 --> 15:19.300 something and I was unaware of it and you were unaware of it. 15:19.840 --> 15:23.032 We want a record, a tamper proof record, a place to point to 15:23.056 --> 15:26.216 where we can say you've told everything you've done with your model 15:26.248 --> 15:29.432 since we did our initial review is written here. I can see it, you can 15:29.456 --> 15:32.660 see it. Regulators can even see it if they need to. 15:32.960 --> 15:35.982 It's a much better way and much lighter way of, 15:36.056 --> 15:39.186 of proceeding rather than the notion of quarterly 15:39.218 --> 15:43.074 or yearly audits which are not going to catch things quickly enough to avoid 15:43.202 --> 15:46.750 potentially very damaging types of issues. 15:47.930 --> 15:51.362 So again, a lot of this 15:51.386 --> 15:55.090 is forward looking, so I'll just keep going through some additional use 15:55.130 --> 15:58.578 cases here. Another one is around journaled model outcomes. 15:58.674 --> 16:02.018 So the actual predictions, the outcomes of a model, Right. 16:02.074 --> 16:05.202 So now you can imagine the problem of, let's say I'm an AI as 16:05.226 --> 16:08.230 a service provider, right. I provide a model, 16:08.570 --> 16:12.210 it makes predictions, that's all my business does. 16:12.250 --> 16:16.322 And then there's other applications that build on my predictions downstream 16:16.386 --> 16:20.270 to build out a full solution. All right, so what happens when 16:20.650 --> 16:24.258 there's an issue, right. A customer encounters some sort of problem. 16:24.394 --> 16:28.248 Right, so let's take automotive driving. So let's say I provide a model that 16:28.354 --> 16:32.508 can detect objects, do visual processing on objects 16:32.524 --> 16:36.092 and let's say I'm an automotive maker that consumes those models 16:36.236 --> 16:39.404 and I build a self driving application 16:39.452 --> 16:42.668 off of them. Okay, well what happens when the car goes 16:42.684 --> 16:45.900 ahead and hits something it shouldn't hit? Very bad situation, 16:45.980 --> 16:49.964 very bad outcome and I want to know exactly 16:50.012 --> 16:53.962 where the problem was. It can't be a two month inquiry about 16:54.156 --> 16:57.570 was this a bug in your model? Was it a bug in my software? 16:58.470 --> 17:01.790 Can't get lawyers involved in that. It's going to take too long. You're going to 17:01.830 --> 17:05.358 be offline for way too long. What you can do, though, is journal 17:05.414 --> 17:08.910 your predictions, right? If I'm an AI, as a service provider, I can journal those 17:08.950 --> 17:12.398 predictions to a decentralized ledger. I can say, 17:12.534 --> 17:15.902 again, I show my work. This is what our software did. And if there's any 17:15.926 --> 17:19.422 discrepancy about what actually happened and 17:19.526 --> 17:22.962 who. What actually happened underneath the hood of 17:22.986 --> 17:27.378 this, it's not something I have to go and request logs for and get 17:27.514 --> 17:30.722 legal involved. We don't want to live in that type of world. 17:30.826 --> 17:33.762 And that is still the world we live in today with a lot of our 17:33.786 --> 17:37.394 cloud providers. So I rely on a cloud provider. I expected to 17:37.402 --> 17:40.194 do something. Hey, there was a bug. Hey, it caused me damage. 17:40.322 --> 17:43.922 What do I do? I can't go look through their logs. I can request their 17:43.946 --> 17:47.202 logs. I can request a root cause analysis, I can ask for 17:47.226 --> 17:50.994 a bunch of stuff. If I'm not satisfied, I can go get lawyers 17:51.042 --> 17:54.498 involved. Slow process. And the only reason we've put up with that 17:54.554 --> 17:57.954 so long is just the blast radius for these types of things has been easier 17:58.002 --> 18:01.346 to mitigate. They're much harder to mitigate in the gen AI world, 18:01.418 --> 18:04.150 which is why we think a different approach is necessary. 18:04.810 --> 18:08.530 All right. Automation types of use cases. Again, I like to say 18:08.570 --> 18:12.178 automate. We're not talking about workflows 18:12.194 --> 18:15.772 here. Workflows involves. Implies people are going to be in 18:15.796 --> 18:19.276 the mix, right? We think the vision here is people list 18:19.348 --> 18:23.132 types of automation. So chatbots could be an 18:23.156 --> 18:26.920 interesting use case or example of a use case here where we say, 18:27.300 --> 18:31.116 if I deploy just a simple customer service type 18:31.148 --> 18:33.400 of, type of chatbot here, 18:34.740 --> 18:39.212 there's a whole bunch of issues here. Then how 18:39.236 --> 18:43.120 do we figure out when something goes awry? With one of these, 18:43.300 --> 18:46.328 with one of these chatbots that we purchase, Right. How do we go ahead and 18:46.384 --> 18:49.816 trigger some actions based on it? That makes sense. How do we fully 18:49.848 --> 18:53.800 automate a recovery? How do we fully sort of streamline this 18:53.840 --> 18:57.384 whole process? Another version of. Let's say I release a new version of 18:57.392 --> 19:00.616 a chat bot, which maybe I want to do weekly. How do I educate 19:00.648 --> 19:04.440 the customer service teams who work alongside these chatbots with 19:04.480 --> 19:07.336 changes? It's a very real problem I've had and I've seen, 19:07.408 --> 19:11.272 right? You say, hey, we're going to release a feature. I need 19:11.296 --> 19:14.392 to inform the customer support team that this is. We have to do our training, 19:14.456 --> 19:17.880 we have to do our documentation, we have to make sure they're aware that things 19:17.920 --> 19:21.480 are going to change. Okay, but what happens if your changes are now 19:21.520 --> 19:25.032 sourced by an LLM, then what right are 19:25.056 --> 19:28.216 you going to slow everything down and say wait a minute, the actual human beings 19:28.248 --> 19:31.752 who do customer support need to need a couple of days to 19:31.776 --> 19:34.660 look through these changes and make sure they understand things. 19:35.680 --> 19:39.092 Don't think that's going to work, it's going to be too slow. So what we 19:39.116 --> 19:42.740 think though is well, if you automation has to be part of this, right? 19:42.780 --> 19:46.116 Every time stuff is changing with your event stream, 19:46.148 --> 19:49.892 with your model development, you're going to need to find a way to automate these 19:49.916 --> 19:53.572 events into meaningful actions that help the 19:53.596 --> 19:56.820 people who use it be aware of what just changed without having 19:56.860 --> 19:59.760 to go through a click a button. Review this. 20:00.140 --> 20:03.572 Touch this again. It's another plug for a decentralized ledger. 20:03.636 --> 20:07.144 Because decentralized ledgers you can build applications directly on 20:07.152 --> 20:10.680 the event stream in a secure tamper proof way. So it's a use 20:10.720 --> 20:13.752 case and we think it's an important one. All right, 20:13.776 --> 20:17.540 and the last one I'll cover here around sort of forward looking use cases 20:17.840 --> 20:21.048 is they're around model and tokenized data 20:21.104 --> 20:24.776 sets, right? So let's briefly just talk about the exchange of data, 20:24.848 --> 20:27.704 the tokenization of data, the ownership of data, 20:27.872 --> 20:31.800 right? So suppose I want to 20:31.840 --> 20:36.008 go ahead and share my information or my proprietary data 20:36.064 --> 20:40.360 sets with third parties, right? That made like again 20:40.480 --> 20:44.744 use cases. I've tried to do this in the past. You get into heavyweight licensing 20:44.792 --> 20:48.248 deals. It's difficult, right? It's a very difficult problem 20:48.384 --> 20:52.260 to solve. How do you give somebody your data without losing control of it? 20:52.880 --> 20:56.072 It's again a good use case for a decentralized 20:56.136 --> 21:00.312 ledger. You can put something perhaps tokenized on 21:00.336 --> 21:03.514 the ledger that says this is the cryptographic hash of the 21:03.522 --> 21:07.034 data set that we have and or the rows, here's the event stream of the 21:07.042 --> 21:10.810 data as I change it so you don't lose ownership of something even 21:10.850 --> 21:14.138 though the data sets continually changing, right? So again, 21:14.194 --> 21:17.466 is this how else would you do this? Right? I think that's what keeps coming 21:17.538 --> 21:21.002 back to us on prove AI is we know we're going to need to 21:21.026 --> 21:24.426 exchange data, we know we need to change this data continually. 21:24.538 --> 21:27.610 How on earth are you going to share this with third parties without it making 21:27.650 --> 21:30.730 a mess, right? How are you going to prove that you had ownership of this? 21:30.770 --> 21:33.932 How are you going to prove that each of these little change sets is 21:33.956 --> 21:37.692 stuff that you initiated, not some other third party knowing full well that 21:37.716 --> 21:41.280 you may be liable for whatever happens down the stream? 21:41.780 --> 21:44.860 Again, this is probably one of the most forward looking use cases out there. 21:44.900 --> 21:48.572 But again starting Today our thesis at 21:48.596 --> 21:52.520 PROVE AI is all of these things are likely 21:53.300 --> 21:56.588 to come back on us and cause a problem in the near future. Near future 21:56.644 --> 22:00.118 being one to two years. And if they do, then if I'm 22:00.134 --> 22:04.118 struggling with compliance today and how should I think about AI? 22:04.294 --> 22:08.054 We really think that the right way is to start with your data, start with 22:08.062 --> 22:11.638 your event collection and start thinking about these sort of third party 22:11.694 --> 22:15.094 use cases, these sharing use cases, these types of 22:15.102 --> 22:18.854 issues where you need real time event streams that are outside your multiple MLOps 22:18.902 --> 22:22.566 tools and start there and then continue on 22:22.718 --> 22:26.502 and build out the reactions to it exactly as 22:26.526 --> 22:30.236 you need. As an. So if you're interested, you outcome by 22:30.308 --> 22:33.996 we're at booth 4, 3, 5. Again we're an open, 22:34.068 --> 22:37.644 we're an early access product, we have a few initial customers. 22:37.692 --> 22:41.100 But as you heard from all of these use cases, right, it is early and 22:41.140 --> 22:45.100 that's, that's okay, right? Like any of the past big technology 22:45.220 --> 22:48.492 trends, the first step was typically to get your, you know, 22:48.516 --> 22:52.124 get your observability, get your eventing and get your data strategy correct 22:52.172 --> 22:56.152 first or you're going to make a headache for yourself later. And all 22:56.176 --> 22:59.352 things being equal, if you have to write and store this data anyway, why not 22:59.376 --> 23:04.424 choose the right type of storage and the right sort of long term philosophy 23:04.472 --> 23:08.808 here? Because there's really no downside to it, right? There's only potential upside. 23:08.984 --> 23:12.488 So with that I think that's about time and thank you. Great, 23:12.544 --> 23:13.220 thank you. 23:16.000 --> 23:18.360 We don't have a lot of time for questions, but we do have a break. 23:18.400 --> 23:21.736 So we have a little bit of a buffer. So one common element 23:21.768 --> 23:24.712 for a lot of the gen AI platform is that people may be building for 23:24.736 --> 23:28.750 internal use or combining together with for example a ChatGPT 23:28.830 --> 23:32.206 or a Claude or whatever is that they all learn from user 23:32.238 --> 23:35.886 feedback. Right. So essentially the LLM is learning and improving and the vector 23:35.918 --> 23:39.294 databases are adjusting based on the users 23:39.342 --> 23:43.534 actually interacting with the data set that's there. So obviously 23:43.582 --> 23:47.198 one could sort of be retraining the model on 23:47.254 --> 23:51.182 new data, but that doesn't necessarily take into account the training that 23:51.206 --> 23:55.130 occurs as a result of somebody saying, now wait, that wasn't actually the best 23:55.170 --> 23:58.522 Chinese restaurant on the Upper west side. You should have also looked at 23:58.546 --> 24:02.362 these Chinese restaurants. Well, at that point the LLM should be 24:02.386 --> 24:06.698 learning from the user feedback. So how does one document 24:06.794 --> 24:10.874 changes to the overall corpus of information and potential relevance 24:10.922 --> 24:15.162 of results when it's coming from user feedback? So that's exactly. 24:15.226 --> 24:19.632 Yeah, that's a great example of the problem here. Right. So as 24:19.656 --> 24:23.344 the AIs themselves, as whatever update 24:23.392 --> 24:26.960 mechanism in there is, we imagine a case 24:27.000 --> 24:31.184 where they're writing these events to the decentralized 24:31.232 --> 24:34.896 ledger, basically to tie back who exactly was the actor. 24:35.008 --> 24:38.448 Can we cryptographically be sure of who made this change? 24:38.584 --> 24:42.016 Another benefit of decentralized ledger. And then 24:42.088 --> 24:45.392 can you figure out exactly what point in time is? And most importantly, could you 24:45.416 --> 24:49.216 replay could you basically use a decentralized ledger as, 24:49.368 --> 24:53.008 like, a database ledger? So if there is a problem and then there's a change 24:53.064 --> 24:56.192 that somehow the AIs came 24:56.216 --> 25:00.416 to the wrong conclusion here, could you roll back and replay in reverse 25:00.528 --> 25:03.696 back to a known good state? Right. So, yes, you can. 25:03.768 --> 25:07.488 And you can do all of these things with something like a decentralized ledger. 25:07.664 --> 25:10.640 Great. Thanks so much, and we'll see you guys back here shortly.

On January 20, Donald Trump was sworn in as the 47th President of the United States. His inauguration has prompted a raft of speculation on what it will mean for the rapidly-growing artificial intelligence industry, and whether we should expect more (or less) federal regulation on AI from the United States Government. While the consensus opinion has long maintained that a Trump-led administration is unlikely to push a host of new legislation in Congress – a point which was underscored by its repeal of the Biden executive order on AI safety within its first 24 hours in office – it’s been equally clear that it sees spurring continued AI innovation in the U.S. and beyond as a strategic priority. This point was punctuated by the administration’s subsequent announcement of Stargate, a $500 billion fund focused on building out artificial intelligence infrastructure in the U.S., led by OpenAI, SoftBank, Oracle and MGX. Regardless of some of the speculation that has since arisen around the extent of the fund’s working capital, it’s a powerful signal that this administration wants to accelerate AI development efforts, and that it’s already creating a strong incentive structure for companies focused on AI infrastructure. What comes next? In the absence of clear regulation governing AI best practices, where can companies turn for guidance? Recognizing that the administration is unlikely to take the legislative route, it’s increasingly clear that a standards-based approach is key to realizing effective and lasting AI solutions. Like the earlier rise of major, transformative technologies– cloud computing, database technology and even the internet itself among them – the AI industry must align around broadly-accepted standards to realize a meaningful impact at scale. Review our ISO 42001 FAQs When it comes to AI standards, one clearly rises above the rest in terms of substance, scope and influence: ISO 42001. Published by the widely respected International Organization for Standardization (ISO), ISO 42001 outlines detailed standards for “establishing, implementing, maintaining and continually improving an Artificial Intelligence Management System within organizations.” Its language has been broadly adopted by nearly all of the most substantive AI legislation passed and/or under review to date, including the EU AI Act and the host of state-led bills being currently reviewed in the U.S. Beyond the public sector, ISO 42001 is also being embraced by some of the most significant actors in the AI industry, including AWS, which became the first major cloud service provider to achieve ISO 42001 accreditation for AI services in November 2024, Cognizant and Anthropic. Why ISO 42001 matters For companies looking to scale and manage AI systems responsibly, complying with the ISO 42001 standard is a critically important step. The framework addresses critical issues such as ethical AI deployment, bias mitigation, and oversight, providing a clear roadmap for organizations navigating this complex space. As the administration signals its intent to foster innovation without heavy-handed regulation, businesses must take proactive steps to align with globally accepted standards. Those who act quickly to adopt ISO 42001 will not only enhance their operational resilience but also position themselves as leaders in a rapidly evolving market. Ready to take the next step? Responsible innovation is key to realizing optimal AI outputs – and the industry is aligning around this reality. It’s less about stymying progress and more about providing the “seatbelts” needed to mitigate the damage when accidents inevitably occur during the development process. If your organization is ready to navigate AI standards and deploy responsible AI systems, we’re here to help. Whether you need guidance on achieving ISO 42001 compliance or support in building scalable AI solutions, let’s talk.