The success of agentic AI in financial services depends not just on smarter models, but on an authoritative context data store--one that is accessible, reliable, and governed at scale. Financial services companies have unique needs when it comes to business AI. They operate in one of the most highly regulated sectors while responding to external events that are updated by the second. As a result, the success of agentic AI in financial services depends less on the sophistication of the system and more on the quality, security, and accessibility of the data it relies on. "It all starts with the data," says Steve Mayzak, global managing director of Search AI at Elastic. Agentic AI--systems that can independently plan and take actions to complete tasks, rather than simply generate responses--holds enormous potential for financial services due to its ability to incorporate real-time data and optimize complex workflows.

Language is not neutral; it frames understanding, structures power, and shapes governance. This paper argues that misnomers like cybersecurity and artificial intelligence (AI) are more than semantic quirks; they carry significant governance risks by obscuring human agency, inflating expectations, and distorting accountability. Drawing on lessons from cybersecurity's linguistic pitfalls, such as the 'weakest link' narrative, this paper highlights how AI discourse is falling into similar traps with metaphors like 'alignment,' 'black box,' and 'hallucination.' These terms embed adversarial, mystifying, or overly technical assumptions into governance structures. In response, the paper advocates for a language-first approach to AI governance: one that interrogates dominant metaphors, foregrounds human roles, and co-develops a lexicon that is precise, inclusive, and reflexive. This paper contends that linguistic reform is not peripheral to governance but central to the construction of transparent, equitable, and anticipatory regulatory frameworks.

The development and evaluation of Large Language Models (LLMs) have largely focused on individual capabilities. However, this overlooks the intersection of multiple abilities across different types of expertise that are often required for real-world tasks, which we term cross capabilities. To systematically explore this concept, we first define seven core individual capabilities and then pair them to form seven common cross capabilities, each supported by a manually constructed taxonomy. Building on these definitions, we introduce CrossEval, a benchmark comprising 1,400 human-annotated prompts, with 100 prompts for each individual and cross capability. To ensure reliable evaluation, we involve expert annotators to assess 4,200 model responses, gathering 8,400 human ratings with detailed explanations to serve as reference examples. Our findings reveal that, in both static evaluations and attempts to enhance specific abilities, current LLMs consistently exhibit the "Law of the Weakest Link," where cross-capability performance is significantly constrained by the weakest component. Specifically, across 58 cross-capability scores from 17 models, 38 scores are lower than all individual capabilities, while 20 fall between strong and weak, but closer to the weaker ability. These results highlight the under-performance of LLMs in cross-capability tasks, making the identification and improvement of the weakest capabilities a critical priority for future research to optimize performance in complex, multi-dimensional scenarios.

The field of machine learning (ML) security--and corresponding adversarial ML--is rapidly advancing as researchers develop sophisticated techniques to perturb, disrupt, or steal the ML model or data. It's a heady time; because we know so little about the security of these systems, there are many opportunities for new researchers to publish in this field. In many ways, this circumstance reminds me of the cryptanalysis field in the 1990. And there is a lesson in that similarity: the complex mathematical attacks make for good academic papers, but we mustn't lose sight of the fact that insecure software will be the likely attack vector for most ML systems. We are amazed by real-world demonstrations of adversarial attacks on ML systems, such as a 3D-printed object that looks like a turtle but is recognized (from any orientation) by the ML system as a gun.

AI systems are becoming increasingly popular and central in many industries. They decide who might get a loan from the bank, whether an individual should be convicted, and we may even entrust them with our lives when using systems such as autonomous vehicles in the near future. Thus, there is a growing need for mechanisms to harness and control these systems so that we may ensure that they behave as desired. One important issue that has been gaining popularity in the last few years is fairness. While usually ML models are evaluated based on metrics such as accuracy, the idea of fairness is that we must ensure that our models are unbiased with regard to attributes such as gender, race and other selected attributes.

When it comes to using AI to automate or provide productivity benefits in a business, it's more likely than not that the success or failure of any implementation will come down to humans, not AI. To start with, AI solutions are designed and built by humans, and so may be developed according to certain expectations about their users that don't hold true. Or AI might accidentally embed biases that their developers are unaware of, or take insufficient care to address. There are also many potential barriers to adoption of AI technologies from the customer side, including a lack of awareness and understanding, a lack of budget, a lack of skilled people, and a lack of management support. Stakeholders in an AI implementation (including decision makers, technology providers, business units, departments, and individual employees) may have divergent needs, priorities, and objectives, and not all of them will benefit equally from an AI implementation.

The Pentagon's Joint Artificial Intelligence Center is accelerating work as it moves toward what its new director calls "JAIC 2.0." Marine Corps Lt. Gen. Michael Groen -- who took the helm of the organization Oct. 1 -- said the center aims to implement transformational change. "The early days of the JAIC were all about building AI -- solving mostly small problems or small AIs, connecting data sources to problems, to algorithms," he said Nov. 6. "We were really about products, … [and] it was seeding the environment to kind of show what was possible." While the center's 30-plus different projects are all valuable, making products only is not sufficient to transform the Defense Department, he said during an online event hosted by the Center for Strategic and International Studies, a Washington, D.C.-based think tank.

Future research has found that Cybersecurity is directed at human behavioral and emotional factors. Out of this discovery, questions arise. Why do people really commit cybercrime? Is it out of greed, power, negligence, fear, pressure? What are the motives and intentions of these acts?

Sunny Mishra, RPA CoE - Consulting Architect at ExxonMobil at ExxonMobil Great deck, but I have some minor issues. As a universal law, we cannot teach machines more intelligence than what we have at this point in time. So, instead of calling "Artificial Intelligence", we should drop the word "Artificial" and the word "Intelligence". I do not believe that there is any "artificiality" to any intelligence. First of all, Intelligence is gained/learned from us following "Rules" and Data" that we have associated with since we are born. This learning process dictates our outcome and that is fixed. There is no such thing as "Gut Feeling". It does not exist....me simply make it up to make any point heard across. So no matter how big or complex machines we build, it will only learn to behave by the "Rules" and the associated "Data", which always has a "fixed" outcome or result, same as ours. By laws of universe, without evolving, we would have remained as cave dwellers. So, every day of our life, we observe new rules and results which evolves us to the next level. But, If for example, I am locked up in a dark room, isolated from observing any new rules or data or results, I will be at the same level of intelligence as the day I get locked in. Similarly, if we cannot generate any new intelligence in isolation, we cannot feed the robots any new rules and hence they will remain at a certain level of intelligence for ever. What I am trying to say here is "...AI will never be more intelligent than its creator...". 3 months ago Reply Are you sure you want to Yes No Your message goes here Great deck, but I have some minor issues. As a universal law, we cannot teach machines more intelligence than what we have at this point in time. So, instead of calling "Artificial Intelligence", we should drop the word "Artificial" and the word "Intelligence". I do not believe that there is any "artificiality" to any intelligence. First of all, Intelligence is gained/learned from us following "Rules" and Data" that we have associated with since we are born.

Sunny Mishra, RPA CoE - Consulting Architect at ExxonMobil at ExxonMobil Great deck, but I have some minor issues. As a universal law, we cannot teach machines more intelligence than what we have at this point in time. So, instead of calling "Artificial Intelligence", we should drop the word "Artificial" and the word "Intelligence". I do not believe that there is any "artificiality" to any intelligence. First of all, Intelligence is gained/learned from us following "Rules" and Data" that we have associated with since we are born.