Patrick Ciriello

The U.S. Department of Labor has released its Artificial Intelligence Literacy Framework, providing states, workforce agencies, and education partners with a structured resource to strengthen AI readiness across the public workforce system. This framework marks an important step in advancing equitable access to high-quality AI literacy and preparing communities for an evolving digital economy. #CTE #EUPConnect

2

Just tested Nano Banana 2 vs. Pro on turning the U.S. Department of Labor's AI Literacy Framework into a clean visual, and Nano Banana 2 pulls ahead. What I like: -Cleaner 5-tier structure with better visual progression -Sharper typography and icon differentiation -Improved information hierarchy—more scannable, faster to approve -Search grounding for accurate workforce data integration For compliance collateral and training materials, those details compound. Fewer iteration cycles. Better first-pass quality. Pro isn't obsolete but if you're making infographics for your programs, NB2's reasoning engine shows real improvement. Another W for Google Gemini! What's your experience with image gen tools in your workflow?

3

"Multiple studies, properly and critically synthesized, give us a still-imperfect but more three-dimensional view, as we’ve seen with the three studies discussed here. They collectively reveal some clear signals: usage is growing rapidly, that usage is concentrated on a relatively small set of practical tasks, automation is on the rise, and use cases that have to do with human connection make the headlines because of how deeply they’re felt and needed." https://lnkd.in/gpC6e4KH

2

Cybersecurity is for everyone. Use this 10-step checklist to strengthen your digital defenses, protect sensitive data, and reduce risks from phishing, weak passwords, and outdated systems.

3

The network never lies. And neither does the data. After 10+ years managing IT infrastructure across corporate, international, and nonprofit environments, I've learned one thing: The fundamentals always hold. Routing protocols, network segmentation, systems hardening. These aren't just technical skills. They're a way of thinking. Structured. Methodical. Problem-first. What's changed is where that thinking is being applied. Lately, I've been exploring how infrastructure expertise translates into the AI space, and honestly, the overlap caught me off guard. The same instinct that catches a misconfigured VLAN catches a flawed AI response. The same mindset that maps network dependencies is perfect for understanding where AI systems break down. Infrastructure engineers are built for this moment. We just haven't fully stepped into it yet. For any IT professional watching this space from the sideline, your technical depth is not a baseline, it's an edge. AI is not a separate world. It's just another system. And we've been reading systems our whole careers. The future isn't infrastructure or AI. It's both, and the people who understand how things actually work under the hood will always have a seat at the table.

12

I am not a software developer. I’m an IT director who leads teams and builds organizational capabilities in nonprofit IT environments. But I just created and shipped a personal software project using AI coding tools over 10 nights and weekends earlier this month. And I learned a lot more than I expected. When it comes to AI, I’m neither in the hype camp nor am I in the doom camp. I’m a practitioner who wanted to see what these tools can actually do when pointed at a real project, building out a content management system (CMS) for my website. The answer is quite a lot, but only with significant direction, correction, and judgment calls along the way. Starting today, I’m sharing that experience in a four-part series. What worked, what didn’t work, what required intervention, and what I learned about software practices, scope creep, and not burning out while shipping quickly. This isn’t a how-to guide to build your own blog CMS, nor is this a prompt engineering guide for AI coding tools. This is an honest accounting from someone who stretched beyond his usual work building something useful. Up first? Direction, delegation, and why this wasn’t “vibe coding.” https://lnkd.in/gpY8VdRE #NonprofitIT

6

The difference between good security and verified security? ISO 27001. This isn’t just compliance; it’s a blueprint for resilience. When governance, people, and technology align, your security posture becomes a measurable, auditable advantage. Ready to elevate trust, reduce risk, and prove it to customers and regulators? Arpit Garg Prasen P. Jitendra Khimavat Narayanan Rajendran #ISO27001 #InfoSec #CyberSecurity #Audit #RiskManagement #Compliance #ISMS #GRC #SecurityLeadership

8

1 Comment

Part 4 of my MetaArchivist series, “Bones of Accountability,” is now live. This installment looks at a sector that solved the reconstruction problem long before AI arrived. Financial markets learned the hard way that automated systems cannot be governed without evidence. After the Flash Crash in 2010, regulators had to recreate market behavior from logs, timestamps, order books, development records, and change histories. That work reshaped global expectations for audit trails, lifecycle documentation, and the ability to explain what a system did at a specific moment in time. What stands out is how closely these practices resemble what we now call algorithm preservation. Markets require a design-phase evidential layer and an operational-phase audit trail. They require version histories and change control. They require documentation strong enough to recreate events after the fact. In other words, the logic that underpins the EU AI Act has been operating in finance for years. Part 4 explores these parallels and their implications for AI governance, records management, and the growing need to preserve the reasoning and behavior of automated systems. Read Part 4 here: https://lnkd.in/ewe4U5Pv

19

1 Comment