Quote for the day:
"With the new day comes new strength and new thoughts." -- Eleanor Roosevelt
Smaller, Smarter, Faster: AI Will Scale Differently in 2026
"Technology leaders face a pivotal year in 2026, where disruption, innovation
and risk are expanding at unprecedented speed," said Gene Alvarez, distinguished
vice president analyst at Gartner. "The top strategic technology trends
identified for 2026 are tightly interwoven and reflect the realities of an
AI-powered, hyperconnected world where organizations must drive responsible
innovation, operational excellence and digital trust." The centerpiece of that
thesis is the pivot from large, general-purpose LLMs to domain-specific language
models, or DSLMs, and modular multiagent systems, MAS, designed to execute and
audit business workflows. DSLMs promise higher accuracy, lower downstream
compliance risk and cheaper inference costs; MAS promise orchestration and
scale. ... The back half of Gartner's report is a sober reminder of the price of
admission. First is geopatriation. This is the C-suite-level trend of yanking
critical data and apps out of global public clouds and moving them to local or
"sovereign" clouds. Driven by regulations like Europe's GDPR and fears over the
US CLOUD Act, this market is exploding. Second, the security model is flipping.
Gartner's Preemptive Cybersecurity trend predicts a massive shift, forecasting
that 50% of IT security spending will move from "detection and response" to
"proactive protection" by 2030, up from less than 5% in 2024. Today’s security leaders must adopt an asymmetric mindset
We’ve built an unbalanced view of threats. We pour resources into the risks we
know how to manage — firewalls, access control, guard contracts — while
neglecting the ones that move fastest and cut deepest: hybrid, cross-domain, and
narrative-driven threats. Consider the Salt Typhoon campaign in 2024.
State-linked actors compromised multiple U.S. telecom networks for nearly a
year, breaching routers, core systems, and even National Guard networks. What
began as a cyber incident rippled across national security. Or, the hybrid
criminal case in which a fake recruiter on LinkedIn lured a corporate employee
into downloading malware while coordinating physical intimidation. Digital,
physical, and psychological tactics in one operation. ... Asymmetric actors win
by exploiting tempo, surprise, and blind spots. As the former U.S. Army
Asymmetric Warfare Group explained, its mission was to “identify critical
asymmetric threats… through global first-hand observations,” enabling rapid
adaptation in a shifting threat environment. That’s the same level of insight
security leaders should demand whether from small teams or entire corporations.
They don’t respect our categories. They will hit us digitally, physically, and
reputationally in whatever sequence maximizes confusion and slows our response.
They’ll use low-cost tools to cause high-cost damage: small moves, outsized
effects.Employees keep finding new ways around company access controls
AI, SaaS, and personal devices are changing how people get work done, but the
tools that protect company systems have not kept up, according to 1Password.
Tools like SSO, MDM, and IAM no longer align with how employees and AI agents
access data. The result is what researchers call the “access-trust gap,” a
growing distance between what organizations think they can control and how
employees and AI systems access company data. The survey tracks four areas where
this gap is widening: AI governance, SaaS and shadow IT, credentials, and
endpoint security. Each shows the same pattern of rapid adoption and limited
oversight. ... Organizations now rely on hundreds of cloud apps, most outside
IT’s visibility. Over half of employees admit they have downloaded work tools
without permission, often because approved options are slower or lack needed
features. This behavior drives SaaS sprawl. 70% of security professionals say
SSO tools are not a complete solution for securing identities. On average, only
about two-thirds of enterprise apps sit behind SSO, leaving a large portion
unmanaged. Offboarding gaps make the problem worse. 38% of employees say
they have accessed a former employer’s account or data after leaving the
company. ... Mobile Device Management remains the default control for company
hardware, but security leaders see its limits. MDM tools do not adequately
safeguard managed devices or ensure compliance.Securing APIs at Scale: Threats, Testing, and Governance
API security must be approached as a fundamental element of the design and
development process, rather than an afterthought or add-on. Many organizations
fall short in this regard, assuming that security measures can be patched onto
an existing system by deploying security devices like Web Application Firewall
(WAF) at the perimeter. In reality, secure APIs begin with the first line of
code, integrating security controls throughout the design lifecycle. Even
minor security gaps can result in significant economic losses, legal
repercussions, and long-term brand damage. Designing APIs with inadequate
security practices introduces risks that compound over time, often becoming a
time bomb for organizations. ... APIs are attractive targets for
attackers because they expose business logic, data flows, and authentication
mechanisms. According to Salt Security, 94% of organizations experienced an
API-related security incident in the past year. The threats facing APIs are
constantly evolving, becoming more sophisticated and targeted. ... Given the
complexity and scale of API ecosystems, a proactive and comprehensive testing
strategy is crucial. Relying solely on manual testing is no longer sufficient;
automation is key. ... Technical controls are vital, but without a strong
governance framework, API security efforts can quickly unravel. Without
governance, APIs become a “wild west” of inconsistent standards, duplicated
efforts, and accidental exposure. The Agentic evolution, How Autonomous AI is Re-Architecting the Enterprise
The rise of Agentic AI is leading to a new kind of enterprise that functions
more like a living system. In this model, AI agents and humans work together as
collaborators. The agents handle ongoing operations and optimize outcomes, while
humans provide strategy, creativity, and oversight. Organizations that can
successfully combine human intelligence with machine autonomy will lead the next
era of business transformation. They will move faster, adapt quicker, and make
better use of their data and resources. The Agentic Leap is not only about new
technology; it represents a deeper change in how enterprises think and operate.
It marks the beginning of organizations that are not only supported by AI but
are actively driven and shaped by it. This traditional hierarchy of command is
gradually evolving into a network of intelligent collaboration, where humans and
AI systems continuously exchange information, refine strategies, and act with
shared intent. In this model, humans and AI agents function as true partners.
Agents operate as intelligent executors and problem-solvers, constantly
monitoring data flows, identifying opportunities, and adapting operations in
real time. They can handle repetitive, data-intensive tasks, freeing humans to
focus on higher-order functions such as strategic planning, creative innovation,
and ethical oversight. Humans, in turn, provide contextual understanding,
emotional intelligence, and long-term vision qualities that anchor AI-driven
actions in purpose and responsibility.
6 essential rules for unleashing AI on your software development process - and the No. 1 risk
"AI is not something you can pull out of your toolbox and expect magical things
to happen," cautioned Andrew Kum-Seun, research director at Info-Tech Research
Group. "At least, not right now. IT managers must be prepared to address the
human, workflow, and technical implications that naturally come with AI while
being honest about what AI can do today for their organization." In other words,
get your AI implementation in order before you attempt to apply it to getting
your software development in order. ... As Agile is meant to maintain humanity
in software development, AI needs to support this vision. This must be a core
component of AI-driven Agile development as well. "If leaders are unable to
bridge their intent for AI with the team's concerns, they will likely see
improper use of AI and, perhaps, deliberate sabotage in its implementation,"
said Kum-Seun. Another important step is to "keep all AI explainable by ensuring
the use of AI tools that clearly cite where their suggestions come from -- no
black-box code that cannot be simply verified," said Sopuch. "Human oversight is
a required step. AI can write and refactor code, but humans absolutely must
approve merges, product pushes, or any exceptions. Everything in the process
must be logged, including prompts, outputs, and approvals so that an audit can
easily take place on demand."
The AWS outage post-mortem is more revealing in what it doesn’t say
When AWS suffered a series of cascading failures that crashed its systems for
hours in late October, the industry was once again reminded of its extreme
dependence on major hyperscalers. The incident also shed an uncomfortable
light on how fragile these massive environments have become. In Amazon’s
detailed post-mortem report, the cloud giant detailed a vast array of delicate
systems that keeps global operations functioning — at least, most of the time.
... “The outage exposed how deeply interdependent and fragile our systems have
become. It doesn’t provide any confidence that it won’t happen again. ‘Improved
safeguards’ and ‘better change management’ sound like procedural fixes, but
they’re not proof of architectural resilience. If AWS wants to win back
enterprise confidence, it needs to show hard evidence that one regional incident
can’t cascade across its global network again. Right now, customers still carry
most of that risk themselves.” ... Ellis agreed with others that AWS didn’t
detail why this cascading failure happened on that day, which makes it difficult
for enterprise IT executives to have high confidence that something similar
won’t happen in a month. “They talked about what things failed and not what
caused the failure. Typically, failures like this are caused by a change in the
environment. Someone wrote a script and it changed something or they hit a
threshold. It could have been as simple as a disk failure in one of the nodes. I
tend to think it’s a scaling problem.”
Five Real-World Ways AI Can Boost Your Bank’s Operations
Use of artificial intelligence decisioning has already had time to prove itself,
and the results have been strong, according to Daryl Jones, senior director. The
fit varies from one institution to another, "but the lift, overall, has been
unquestionable," said Jones. He said institutions using AI in lending decisions
have generally seen healthy increases in approvals, with solid results. One
caveat is that as aspects of loan decisions transition to AI, institutions have
to be careful how human lenders influence the software development process. ...
Technology has long been a mainstay for antifraud, according to John Meyer,
managing director. "We’ve had machine learning algorithms since the 1990s," said
Meyer, but today’s antifraud applications of AI go a step beyond. He explained
that the old technology could evaluate a few data points "on day two," once the
damage was already done. By contrast, AI-based techniques can screen and surface
instances truly needing human evaluation, according to Meyer. Such applications
include verifying that paper checks are genuine. Meyer noted that check fraud
remains a significant issue for the banking industry in spite of the rise of
digital transactions. ... Even in a modern banking office, documents can be a
rat’s nest. "We had a client on the West Coast that wanted to centralize all of
its operational documents," said Clio Silman, managing director.
Context engineering: Improving AI by moving beyond the prompt
It isn’t a new practice for developers of AI models to ingest various sources of
information to train their tools to provide the best outputs, notes Neeraj
Abhyankar, vice president of data and AI at R Systems, a digital product
engineering firm. He defines the recently coined term context engineering as a
strategic capability that shapes how AI systems interact with the broader
enterprise. ... Context engineering will be critical for autonomous agents
trusted to perform complex tasks on an organization’s behalf without errors, he
adds. ... Context engineering is an “architectural shift” in how AI systems are
built, adds Louis Landry, CTO at data analytics firm Teradata. “Early generative
AI was stateless, handling isolated interactions where prompt engineering was
sufficient,” he says. “However, autonomous agents are fundamentally different.
They persist across multiple interactions, make sequential decisions, and
operate with varying levels of human oversight.” He suggests that AI users are
moving away from the approach of, “How do I ask this AI a question?” to “How do
I build systems that continuously supply agents with the right operational
context?” “The shift is toward context-aware agent architectures, especially as
we move from simple task-based agents to autonomous agentic systems that make
decisions, chain together complex workflows, and operate independently,” Landry
adds.
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