Updating Enterprise Technology to Scale to ‘AI Everywhere’
Operational systems with significant unstructured data will face substantial
re-architecting due to generative AI’s ability to make use of previously
underutilized data sources. In our experience, the most common solution
patterns for generative AI use cases in operational systems fall within the
areas of content generation, knowledge management, and reporting and
documentation ... As generative AI model use cases get deployed across
critical systems and complexity increases, it will put further demands on
collaboration, quality control, reliability, and scalability. AI models will
need to be treated with the same discipline as software code by adopting MLOps
processes that use DevOps to manage models through their life cycle. Companies
should set up a federated AI development model in line with the AIaaS
platform. This should define the roles of teams that produce and consume AI
services, as well as the processes for federated contribution and how datasets
and models are to be shared. Given the pace of evolution of generative AI, it
is also imperative to create AI-first software development processes that
allow for rapid iteration of new solutions and architectures.
EPSS vs. CVSS: What's the Best Approach to Vulnerability Prioritization?
EPSS is a model that provides a daily estimate of the probability that a
vulnerability will be exploited in the wild within the next 30 days. The model
produces a score between 0 and 1 (0 and 100%), with higher scores indicating a
higher probability of exploitation. The model works by collecting a wide range
of vulnerability information from various sources, such as the National
Vulnerability Database (NVD), CISA KEV, and Exploit-DB, along with evidence of
exploitation activity. ... By considering EPSS when prioritizing
vulnerabilities, organizations can better align their remediation efforts with
the actual threat landscape. For example, if EPSS indicates a high probability
of exploitation for a vulnerability with a relatively low CVSS score, security
teams might consider prioritizing that vulnerability over others that may have
higher CVSS scores but a lower likelihood of exploitability. ... Intruder
is a cloud-based security platform that helps businesses manage their attack
surface and identify vulnerabilities before they can be exploited. By offering
continuous security monitoring, attack surface management, and intelligent
threat prioritization, Intruder allows teams to focus on the most critical
risks while simplifying cybersecurity.
How To Embrace The Enterprise AI Era
As enterprises rush to adopt AI technologies, there's a growing concern about
the responsible use of these powerful tools. Ramaswamy stresses the importance
of a thoughtful approach to AI implementation: "We mandated very early that
any models that we train needed obviously to only take data that we had free
use rights on, but we said they also need to have model cards so that if there
is a problem with the data source, you can go back, retrain a model without
the data source." ... Developing a robust data strategy is essential for AI
success. Organizations need a clear plan for managing, sharing, and leveraging
data across the enterprise. This includes establishing data governance
policies, ensuring data quality and consistency, and creating a unified data
architecture that supports AI initiatives. A well-designed data strategy
enables companies to break down silos, improve data accessibility, and create
a solid foundation for AI-driven insights and decision-making. Embracing
interoperability is another critical aspect of preparing for the enterprise AI
era. Companies should look for solutions that support open data formats and
easy integration with other tools and platforms.
The Hidden Language of Data: How Linguistic Analysis Is Transforming Data Interpretation
Unlike conventional methods that focus on structured data, linguistic analysis
delves into the complexities of human communication. It examines patterns,
context, and meaning in text data, allowing us to extract trends and insights
from sources like social media posts, customer reviews, and open-ended survey
responses. Linguistic analysis in data science marries principles from the two
fields. From linguistics, we borrow concepts like syntax (sentence structure),
semantics (meaning), and pragmatics (context). These help us understand not
just what words say, but how they’re used and what they imply. On the data
science side, we leverage technologies like machine learning and natural
language processing (NLP). These technologies allow us to automate the
analysis of large volumes of text, identify patterns, and extract meaningful
information at scale. ... Sentiment analysis is the process of determining the
emotional tone behind words. It analyzes language to understand attitudes,
opinions, and emotions expressed within text and identify whether a piece of
text is positive, negative, or neutral.
Is Synthetic Data the Future of AI Model Training?
It is likely that the use of synthetic data will increase in the AI space.
Gartner anticipates that it will outweigh the use of real data in AI models by
2030. “The use of it is going to grow over time, and if done correctly, [it
will] allow us to create more evolved, more powerful, and more numerous models
to inform the software that we're building,” Brown predicts. That potential
future seems bright, but the road there is likely to come with a learning
curve. “Mistakes are going to be made almost undoubtedly in the use of
synthetic data initially. You're going to forget a key metric that would judge
quality of data,” says Brown. “You're going to implement a biased model of
some sort or a model that hallucinates maybe more than a previous model did.”
Mistakes may be inevitable, but there will be new ways to combat them. As the
use of synthetic data scales, the development of tools for robust quality
checks will need to as well. “Just the same way that we've kept food quality
high, we [need to] do the same thing to keep the model quality high,” Hazard
argues.
Are You Sabotaging Your Cybersecurity Posture?
When ITDR entered the picture in 2020, it was in response to a cybersecurity
industry struggling to protect suddenly remote COVID-era workforces with
existing identity and access management (IAM) solutions. ... Organizations
should never attempt to solve cybersecurity issues they’re not prepared to
handle. Investing in the right specialists — whether in-house or externally —
and ongoing training is essential to maintaining strong defenses. Your
organization will fall behind quickly if your team isn’t continuously
evolving. Where business leaders are concerned, cybersecurity is often an
attractive place to trim expenses. But businesses simply cannot cut their
cybersecurity budget and hope they don’t suffer a breach. Hackers aren’t
stopping, so you can’t either. ... Operating on an “it won’t happen to us”
mindset will always get your organization in trouble. When it comes to
strengthening your organization’s cybersecurity posture, a shift from a
reactive to a proactive mindset is crucial to staying ahead of evolving
threats and preventing costly and damaging breaches. A comprehensive,
identity-focused cybersecurity is the best way to proactively defend against
threats.
Millions of Kia Vehicles Open to Remote Hacks via License Plate
The researchers found that it was relatively easy to register a Kia dealer
account and authenticate it to the account. They could then use the generated
access token to call APIs reserved for use by dealers, for things like vehicle
and account lookup, owner enrollment, and several other functions. After some
poking around, the researchers found that they could use their access to the
dealer APIs to enter a vehicle's license-plate information and retrieve data
that essentially allowed them to control key vehicle functions. These included
functions like turning the ignition on and off, remotely locking and unlocking
vehicles, activating its headlights and horn, and determining its exact
geolocation. In addition, they were able to retrieve the owner's personally
identifying information (PII) and quietly register themselves as the primary
account holder. That meant they had control of functions normally available
only the owner. The issues affected a range of Kia model years, from 2024 and
2025 all the way back to 2013. With the older vehicles, the researchers
developed a proof-of-concept tool that showed how anyone could enter a Kia's
vehicle license plate info and in a matter of 30 seconds execute remote
commands on the vehicle.
How AI is reshaping accounting
For a while, the finance industry started to consider how to provide better
information to guide investment decisions beyond just financial performance
and ESG through integrated reporting. The term has fallen out of vogue. ...
The corollary to continual close is that businesses will be able to make
decisions using real-time data. Forrester predicts that over 70% of SMBs will
integrate real-time data into financial decisions, empowering them to drive
growth and innovation by 2030. Harris acknowledges that today, not all
business is captured in real-time. Existing tools and infrastructure are
insufficient to capture everything with the assurance that it is reliable. So,
accounting data can get out of step by a few days to weeks. The vision is that
with the right technology, particularly AI, they can take that delay down to
zero to keep accounting data in lockstep with the business.
New
opportunities The last prediction is that AI will automate many routine tasks
and free accountants to focus on strategic thinking and provide business
insights. This will create opportunities for accountants to expand into new
roles that improve business strategy and facilitate innovation.
Harnessing AI and knowledge graphs for enterprise decision-making
Whether a company’s goal is to increase customer satisfaction, boost revenue,
or reduce costs, there is no single driver that will enable those outcomes.
Instead, it’s the cumulative effect of good decision-making that will yield
positive business outcomes. It all starts with leveraging an approachable,
scalable platform that allows the company to capture its collective knowledge
so that both humans and AI systems alike can reason over it and make better
decisions. Knowledge graphs are increasingly becoming a foundational tool for
organizations to uncover the context within their data. What does this look
like in action? Imagine a retailer that wants to know how many T-shirts it
should order heading into summer. A multitude of highly complex factors must
be considered to make the best decision: cost, timing, past demand, forecasted
demand, supply chain contingencies, how marketing and advertising could impact
demand, physical space limitations for brick-and-mortar stores, and more. We
can reason over all of these facets and the relationships between using the
shared context a knowledge graph provides.
Data Blind Spots and Data Opportunities: What Banks and Credit Unions May Be Missing
Financial services leaders understand that getting the deal done is only half
the battle. Effective execution of a merger or acquisition is famously
difficult: across all industries, between 70% and 90% of mergers and
acquisitions fail to achieve their intended goals or create shareholder value,
according to research by McKinsey, Harvard Business Review and others. These
failures can be due to a range of factors, including poor strategic fit,
cultural clashes, integration challenges, or failure to realize projected
synergies. For financial institutions in particular — FDIC data since 2019
indicates that some 4-5% of insured depositories merge annually—M&A can be
a way of life and effective integration demands a data-first approach. When
management data — such as financial reports, risk assessments, and
accountholder information—is consolidated quickly, both institutions can
harmonize their strategies, avoid duplicative efforts, and identify risks and
synergies earlier. This data integration allows leadership teams to monitor
KPIs, streamline operations, and make informed decisions that align with the
newly combined FI’s objectives.
Quote for the day:
"Effective team leaders realize they
neither know all the answers, nor can they succeed without the other members
of the team." -- Katzenbach & Smith
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