February 2026 Tech Upload
| The NEW Digital Alliance would like to thank StellarBlue.ai and Tanduo Technical Partners for their support as In-Kind investors! |  |
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NEW Digital News
Now Accepting Applications: Investor Mentorship Program
Applications are now open for the NEW Digital Alliance’s Investor Mentorship Program, a high-impact professional development experience designed to connect high-performing IT professionals across organizations through a proven mentorship framework.
The 2026 program will run March 12 through July 16, pairing mentors and mentees in meaningful one-to-one relationships focused on career growth, skill development, and network expansion.
What’s in it for you?
Participants in the Investor Mentorship Program will:
- Grow targeted skillsets through a guided mentor/mentee relationship
- Gain outside perspective from local IT talent
- Build relationships with like-minded professionals
- Expand their professional network across Northeast Wisconsin
Program Goal
The goal of the program is simple but powerful: to connect high performers across organizations using a structured mentorship approach that accelerates careers while strengthening professional networks within our region’s digital ecosystem.
Time Commitment
Participants should expect a manageable and intentional commitment:
- Two one-on-one sessions per month
- One hour per session
- Approximately three months total
Who Can Apply
Applications are open to both mentors and mentees.
Please note: participants must work for a company that is an investor of the NEW Digital Alliance to be eligible.
If you’re looking to grow, give back, and strengthen connections across the region’s digital community, we encourage you to apply and be part of this impactful experience.
Upcoming northeast Wisconsin IT events
WIT: Sip & Sync Green Bay
Thursday, February 5
8:00 – 9:00 a.m.
Karvana Coffee House (Green Bay)
Organizer: Women in Technology
WIT: Sip & Sync Appleton
Wednesday, February 18
8:30 – 9:30 a.m.
Copper Rock Coffee Company (Appleton – College Ave)
Organizer: Women in Technology
Generative AI for Business Growth
February 20 – April 3
Asynchronous
Virtual
Organizer: gener8tor
All My Xs: The Experiences That Shape Technology
Wednesday, February 25
11:15 a.m. – 1:30 p.m.
Fox Valley Technical College
Organizer: Women in Technology Wisconsin
Cybersecurity Roundtable: The State of Cybersecurity 2026
Thursday, February 26
11:30 a.m. – 12:30 p.m.
Virtual (Zoom)
Organizer: NEW Digital Alliance
WiTForGirls: Bites & Bytes
Thursday, February 26
4:30 – 6:30 p.m.
Schneider (The Grove – Ashwaubenon)
Organizer: Women in Technology Wisconsin
How to deliver effective criticism
(Photo credit: Beacon Hill)
By Beacon Hill
Effective criticism isn’t just about pointing out what went wrong — it’s about making feedback clear, actionable, and developmental. Beacon Hill’s latest guide breaks down the elements that separate helpful feedback from unhelpful judgment, offering practical advice for managers and technical leads alike.
The article starts by outlining what doesn’t work — vague, judgmental, overly negative, or poorly timed criticism — and why those styles often trigger defensiveness rather than improvement. Instead, it emphasizes objective, evidence‑based feedback that focuses on behavior and outcomes rather than personality.
Key recommendations you’ll appreciate as a technical leader include:
- Use specific examples and data to ground your feedback — abstraction doesn’t drive behavior change.
- Make it a dialogue, not a monologue: frame your feedback as a conversation with pauses for the recipient’s perspective.
- Choose timing and context carefully — prompt, private discussions are more effective than surprises during all‑hands or reviews.
- Follow up and support improvement, potentially linking feedback to mentoring, tooling, or specific training paths.
This article is a useful reference for technical managers, project leads, and anyone responsible for code reviews, performance feedback, or team mentoring, especially where criticism can influence retention, quality, and culture.
Read the full article for practical techniques that help turn critical feedback into real performance gains.
Wealth and asset management leaders call cybersecurity a major threat. How should they tackle it?
(Image credit: Getty Images)
By Tom Wojcinski
Wipfli
Cyber risk is now front and center for wealth and asset management firms with roughly two‑thirds of executives calling it a top concern heading into 2026, yet many unsure how to operationalize stronger defenses.
This short guide breaks down the distinct threat vectors facing these firms from large‑scale attacks and client‑fund fraud to regulatory scrutiny from the SEC should cybersecurity controls be deemed insufficient.
More importantly for technical teams, the article offers a practical framework for actionable improvements, including:
- Reframing cybersecurity as a strategic growth enabler rather than a cost center, since reputational damage from breaches can directly undercut client trust and business expansion.
- Leveraging advisory support to map gaps in current defenses and design upgrades to people, processes, and technology.
- Conducting penetration tests and scenario simulations — including phishing and ransomware simulations — to identify vulnerabilities before attackers do.
- Incorporating tabletop exercises to practice incident response across scenarios, clarify roles and communications, and harden response cadences ahead of actual breaches.
For mid‑sized and smaller firms that often lack large in‑house cybersecurity teams, this article is a useful, tactical primer on where to start improving posture, prioritize risk remediation, and align cybersecurity with broader enterprise resilience strategies.
Read the full guide to bridge the gap between awareness and action on cybersecurity risk in wealth and asset management.
Navigating CMMC 2.0: Expert Guidance from a Certified Assessor
(Photo credit: HBS)
If your business works with the Department of Defense, understanding the Cybersecurity Maturity Model Certification (CMMC) process is critical — but it can be confusing. In this video, Todd Heinz, a certified CMMC assessor, walks through the framework the way a trusted advisor would, helping organizations make sense of the requirements without overcomplicating compliance.
Todd covers:
- What actually changed in CMMC 2.0 compared to the original model
- How CMMC aligns with NIST standards — and common areas where companies get tripped up
- What assessors are really looking for during evaluations
- How to move forward efficiently without turning compliance into a runaway project
Whether you’re just starting your CMMC journey or already in the midst of preparation, this session helps you avoid common missteps, sanity-check your current approach, and focus on what matters most.
Watch the full video to gain actionable insights from a CMMC expert and make compliance more manageable.
Other IT News
Hackers are using LLMs to generate malicious JavaScript in real time
(Image credit: Getty Images)
By Emma Woollacott
IT Pro
Cybercriminals are taking advantage of the very tools many organizations are adopting, large language models (LLMs), to create a new breed of phishing and browser-based attacks that are hard to detect and stop. Researchers from Palo Alto Networks’ Unit 42 have uncovered a technique where seemingly benign web pages leverage LLM APIs to generate malicious JavaScript on-the-fly directly in a user’s browser. Because the harmful code is assembled at runtime and delivered from trusted domains, traditional network and static code defenses struggle to spot it and each victim may see a unique, polymorphic payload.
This represents a real shift in attacker tactics: rather than embedding malicious scripts that can be scanned and blocked, attackers are using AI services to craft harmful code only when and where it executes. The article stresses why defenders should look beyond signature-based tools toward runtime behavioral analysis inside browsers, and why controlling unsanctioned LLM use in the enterprise is more important than ever.
Read the full article to understand how this attack works, why existing protections may fail, and what security teams should prioritize next.
Harnessing AI to redefine revenue growth management
(Photo credit: McKinsey & Company)
Reckitt’s AI transformation goes well beyond descriptive analytics or isolated ML use cases. In this McKinsey case study, the company outlines how it built RGMx, an AI-driven revenue growth management platform that operationalizes pricing, promotion, assortment, and mix optimization across geographies. At its core, RGMx combines standardized enterprise data models, automated data pipelines, and machine-learning–based forecasting and optimization engines to run continuous scenario simulations under changing market conditions.
The article highlights the often-overlooked engineering and governance work behind the scenes: harmonizing fragmented commercial data, embedding analytics directly into planning workflows, enforcing model transparency and explainability, and putting guardrails around decision rights so AI recommendations augment — rather than override — human judgment. Instead of one-off models, Reckitt focused on repeatable, scalable analytics products that could be versioned, monitored, and improved over time. The result is a system capable of dynamically stress-testing pricing and promotional strategies, improving margin resilience, and aligning finance, sales, and supply chain teams around a shared, data-driven decision framework.
A strong example of what it takes to move from AI pilots to production-grade systems that deliver sustained commercial value.
Where Tech Leaders and Students Really Think AI Is Going
(Photo credit: Wired staff : Getty Images)
By Brian Barrett
WIRED
WIRED’s latest “For Future Reference” piece brings together first-hand perspectives from AI industry leaders, technologists, and students to map where AI is practically headed — beyond hype. Rather than another speculative essay, the article uses interviews from a WIRED event to benchmark sentiment on how deeply AI is embedded in daily workflows, its growing role in mission-critical domains like health and education, and the hard trade-offs organizations and developers are wrestling with.
What emerges is a realistic picture of AI in the here and now: widespread everyday use of LLMs for decision support and problem solving, expanding enterprise and consumer deployments (including health-related assistants), and persistent gaps in trust, safety, and governance frameworks. Leaders emphasize the need for robust pre-launch safety testing, clear accountability for harms, and thoughtful product design, while students and researchers highlight concerns about job displacement, data privacy, and cognitive reliance on AI.
For technical readers, this article is valuable not for abstract futurism but for grounded insight into how practitioners and future professionals are interpreting the technology’s integration, risks, and social impact today — and how those perceptions are shaping what comes next.
Read the full article to see the full range of views and how they inform both the promise and pitfalls of AI’s evolving role in society.
IoT Platforms vs Open Source: How Implementation Teams Should Really Decide
(Photo credit: IoT For All)
By Iotellect
IoT For All
Choosing the right IoT foundation isn’t just a tooling debate, it’s an architectural decision that shapes your product’s long-term scalability, operational model, and cost structure. This IoT For All piece stresses that the classic “open source vs. IoT platform” framing often misses the big lifecycle implications of each approach.
Structured IoT platforms provide built-in services for device connectivity, data ingestion, multi-tenant interfaces, and visualization, compressing early development timelines and reducing up-front engineering effort. Open-source stacks, on the other hand, offer maximum flexibility and control, with broad developer talent pools and full access to source code but typically with more upfront integration work.
The real trade-offs become apparent as products mature: platform-based solutions can absorb complexity in deployment, operations, and multi-customer scaling, while open-source stacks require teams to own ongoing concerns like security patching, governance, and heterogeneous device support. Economic costs also shift over time from licensing vs. engineering effort to the operational burden of managing scaling, compliance, and uptime SLAs.
A solid read for IoT architects and engineering leaders weighing lifecycle resilience, operational control, and long-term product strategy beyond just MVP development speed.