Small language models and open source are transforming AI
From an enterprise perspective, the advantages of embracing SLMs are
multifaceted. These models allow businesses to scale their AI deployments
cost-effectively, an essential consideration for startups and midsize
enterprises that need to maximize their technology investments. Enhanced agility
becomes a tangible benefit as shorter deployment times and easier customization
align AI capabilities more closely with evolving business needs. Data privacy
and sovereignty (perennial concerns in the enterprise world) are better
addressed with SLMs hosted on-premises or within private clouds. This approach
satisfies regulatory and compliance requirements while maintaining robust
security. Additionally, the reduced energy consumption of SLMs supports
corporate sustainability initiatives. That’s still important, right? The pivot
to smaller language models, bolstered by open source innovation, reshapes how
enterprises approach AI. By mitigating the cost and complexity of large
generative AI systems, SLMs offer a viable, efficient, and customizable path
forward. This shift enhances the business value of AI investments and supports
sustainable and scalable growth.
The Impact and Future of AI in Financial Services
Winston noted that AI systems require vast amounts of data, which raises
concerns about data privacy and security. “Financial institutions must ensure
compliance with regulations such as GDPR [General Data Protection Regulation]
and CCPA [California Consumer Privacy Act] while safeguarding sensitive
customer information,” he explained. Simply using general GenAI tools as a
quick fix isn’t enough. “Financial services will need a solution built
specifically for the industry and leverages deep data related to how the
entire industry works,” said Kevin Green, COO of Hapax, a banking AI platform.
“It’s easy for general GenAI tools to identify what changes are made to
regulations, but if it does not understand how those changes impact an
institution, it’s simply just an alert.” According to Green, the next wave of
GenAI technologies should go beyond mere alerts; they must explain how
regulatory changes affect institutions and outline actionable steps. As AI
technology evolves, several emerging technologies could significantly
transform the financial services industry. Ludwig pointed out that quantum
computers, which can solve complex problems much faster than traditional
computers, might revolutionize risk management, portfolio optimization, and
fraud detection.
Is Your Data AI-Ready?
Without proper data contextualization, AI systems may make incorrect
assumptions or draw erroneous conclusions, undermining the reliability and
value of the insights they generate. To avoid such pitfalls, focus on
categorizing and classifying your data with the necessary metadata, such as
timestamps, location information, document classification, and other relevant
contextual details. This will enable your AI to properly understand the
context of the data and generate meaningful, actionable insights.
Additionally, integrating complementary data can significantly enhance the
information’s value, depth, and usefulness for your AI systems to analyze. ...
Although older data may be necessary for compliance or historical purposes, it
may not be relevant or useful for your AI initiatives. Outdated information
can burden your storage systems and compromise the validity of the
AI-generated insights. Imagine an AI system analyzing a decade-old market
report to inform critical business decisions—the insights would likely be
outdated and misleading. That’s why establishing and implementing robust
retention and archiving policies as part of your information life cycle
management is critical.
Generative AI: Good Or Bad News For Software
There are plenty of examples of breaches that started thanks to someone
copying over code and not checking it thoroughly. Think back to the Heartbleed
exploit, a security bug in a popular library that led to the exposure of
hundreds of thousands of websites, servers and other devices which used the
code. Because the library was so widely used, the thought was, of course,
someone had checked it for vulnerabilities. But instead, the vulnerability
persisted for years, quietly used by attackers to exploit vulnerable systems.
This is the darker side to ChatGPT; attackers also have access to the tool.
While OpenAI has built some safeguards to prevent it from answering questions
regarding problematic subjects like code injection, the CyberArk Labs team has
already uncovered some ways in which the tool could be used for malicious
reasons. Breaches have occurred due to blindly incorporating code without
thorough verification. Attackers can exploit ChatGPT, using its capabilities
to create polymorphic malware or produce malicious code more rapidly. Even
with safeguards, developers must exercise caution. ChatGPT generates the code,
but developers are accountable for it
FinOps Can Turn IT Cost Centers Into a Value Driver
Once FinOps has been successfully implemented within an organization, teams
can begin to automate the practice while building a culture of continuous
improvement. Leaders can now better forecast and plan, leading to more precise
budgeting. Additionally, GenAI can provide unique insights into seasonality.
For example, if a resource demand spikes every three days at other
unpredictable frequencies, AI can help you detect these patterns so you can
optimize by scaling up when required and back down to save costs during lulls
in demand. This kind of pattern detection is difficult without AI. It all goes
back to the concept of understanding value and total cost. With FinOps, IT
leaders can demonstrate exactly what they spend on and why. They can point out
how the budget for software licenses and labor is directly tied to IT
operations outcomes, translating into greater resiliency and higher customer
satisfaction. They can prove that they’ve spent money responsibly and that
they should retain that level of funding because it makes the business run
better. FinOps and AI advancements allow businesses to do more and go further
than they ever could. Almost 65% of CFOs are integrating AI into their
strategy.
The convergence of human and machine in transforming business
To achieve a true collaboration between humans and machines, it is crucial to
establish a clear understanding and definition of their respective roles. By
emphasizing the unique strengths of AI while strategically addressing its
limitations, organizations can create a synergy that maximizes the potential
of both human expertise and machine capabilities. AI excels in data
structuring, capable of transforming complex, unstructured information into
easily searchable and accessible content. This makes it an invaluable tool for
sorting through vast online datasets, including datasets, news articles,
academic reports and other forms of digital content, extracting meaningful
insights. Moreover, AI systems operate tirelessly, functioning 24/7 without
the need for breaks or downtime. This "always on" nature ensures a constant
state of productivity and responsiveness, enabling organizations to keep pace
with the rapidly changing market. Another key strength of AI lies in its
scalability. As data volumes continue to grow and the complexity of tasks
increases, AI can be integrated into existing workflows and systems, allowing
businesses to process and analyze vast amounts of information efficiently.
The Crucial Role of Real-time Analytics in Modern SOCs
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Security analysts often spend considerable time manually correlating diverse
data sources to understand the context of specific alerts. This process leads
to inefficiency, as they must scan various sources, determine if an alert is
genuine or a false positive, assess its priority, and evaluate its potential
impact on the organization. This tedious and lengthy process can lead to
analyst burnout, negatively impacting SOC performance. ... Traditional
Security Information and Event Management (SIEM) systems in SOCs struggle to
effectively track and analyze sophisticated cybersecurity threats. These
legacy systems often burden SOC teams with false positives and negatives.
Their generalized approach to analytics can create vulnerabilities and strain
SOC resources, requiring additional staff to address even a single false
positive. In contrast, real-time analytics or analytics-driven SIEMs offer
superior context for security alerts, sending only genuine threats to security
teams. ... Staying ahead of potential threats is crucial for organizations in
today's landscape. Real-time threat intelligence plays a vital role in
proactively detecting threats. Through continuous monitoring of various threat
vectors, it can identify and stop suspicious activities or anomalies before
they cause harm.
Architecting with AI
Every project is different, and understanding the differences between projects
is all about context. Do we have documentation of thousands of corporate IT
projects that we would need to train an AI to understand context? Some of that
documentation probably exists, but it's almost all proprietary. Even that's
optimistic; a lot of the documentation we would need was never captured and
may never have been expressed. Another issue in software design is breaking
larger tasks up into smaller components. That may be the biggest theme of the
history of software design. AI is already useful for refactoring source code.
But the issues change when we consider AI as a component of a software system.
The code used to implement AI is usually surprisingly small — that's not an
issue. However, take a step back and ask why we want software to be composed
of small, modular components. Small isn't "good" in and of itself. ... Small
components reduce risk: it's easier to understand an individual class or
microservice than a multi-million line monolith. There's a well-known
paper(link is external) that shows a small box, representing a model. The box
is surrounded by many other boxes that represent other software components:
data pipelines, storage, user interfaces, you name it.
Hungry for resources, AI redefines the data center calculus
With data centers near capacity in the US, there’s a critical need for
organizations to consider hardware upgrades, he adds. The shortage is
exacerbated because AI and machine learning workloads will require modern
hardware. “Modern hardware provides enhanced performance, reliability, and
security features, crucial for maintaining a competitive edge and ensuring
data integrity,” Warman says. “High-performance hardware can support more
workloads in less space, addressing the capacity constraints faced by many
data centers.” The demands of AI make for a compelling reason to consider
hardware upgrades, adds Rob Clark, president and CTO at AI tool provider
Seekr. Organizations considering new hardware should pull the trigger based on
factors beyond space considerations, such as price and performance, new
features, and the age of existing hardware, he says. Older GPUs are a prime
target for replacement in the AI era, as memory per card and performance per
chip increases, Clark adds. “It is more efficient to have fewer, larger cards
processing AI workloads,” he says. While AI is driving the demand for data
center expansion and hardware upgrades, it can also be part of the solution,
says Timothy Bates, a professor in the University of Michigan College of
Innovation and Technology.
How to Bake Security into Platform Engineering
A key challenge for platform engineers is modernizing legacy applications,
which include security holes. “Platform engineers and CIOs have a
responsibility to modernize by bridging the gap between the old and new and
understanding the security implications between the old and new,” he says.
When securing the software development lifecycle, organizations should secure
both continuous integration and continuous delivery/continuous deployment
pipelines as well as the software supply chain, Mercer says. Securing
applications entails “integrating security into the CI/CD pipelines in a
seamless manner that does not create unnecessary friction for developers,” he
says. In addition, organizations must prioritize educating employees on how to
secure applications and software supply chains. ... As part of baking security
into the software development process, security responsibility shifts from the
cybersecurity team to the development organization. That means security
becomes as much a part of deliverables as quality or safety, Montenegro says.
“We see an increasing number of organizations adopting a security mindset
within their engineering teams where the responsibility for product security
lies with engineering, not the security team,” he says.
Quote for the day:
“If you really want to do something,
you will work hard for it.” -- Edmund Hillary
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