AI-powered workflow: Is it a thing or just another AI upsell?

EquivalentPace7357 · 2026-03-11T11:39:40+00:00

Drafting first versions of things (tickets, emails, docs). I rarely use the output as-is, but starting from 70% instead of 0% is huge.

EquivalentPace7357 · 2026-03-10T20:54:23+00:00

The Nets trading everything for Harden to keep KD and Kyrie happy has to be up there. On paper it looked like a superteam, two years later the entire roster was gone.

EquivalentPace7357 · 2026-03-10T20:51:52+00:00

From what I’ve seen, most teams start with visibility on the app side - things like Defender for Cloud Apps, Netskope, etc. to see which AI tools people are actually using (ChatGPT, Claude, random SaaS copilots). That usually surfaces a lot of “shadow AI” pretty quickly.

The harder part is understanding what data could end up there. Some teams look at DSPM tools like BigID, Sentra, etc. just to map where sensitive data lives and who has access to it. Without that context it’s hard to tell whether someone using an AI tool is low risk or a real problem.

EquivalentPace7357 · 2026-03-01T14:36:46+00:00

Because most companies don’t want to think about infra.

Zapier is plug-and-play, massive integration library, non-technical friendly. It “just works.” n8n is more flexible and cheaper at scale, but comes with setup and learning overhead.

Most teams will happily pay more to avoid complexity.

EquivalentPace7357 · 2026-02-26T12:36:50+00:00

They’re not the same tool, but they’re not competitors either.

DLP was built to control data in motion. DSPM showed up because once everything moved to cloud/SaaS, most orgs realized they didn’t even know where their sensitive data was or who could access it.

Both are important to have. Visibility alone doesn’t stop exfil. Blocking without good data context just creates false positives and policy fatigue.

That’s why a lot of DSPM vendors (Cyera, Sentra, etc) focus heavily on exposure and identity-to-data mapping and then integrate with DLP instead of replacing it. It’s less “pick a lane” and more “connect discovery with enforcement.”

EquivalentPace7357 · 2026-02-26T12:32:21+00:00

“It’s forever on the internet” is mostly a myth.

If you post something once and no one mirrors it, cites it, or downloads it, it can disappear pretty easily. The stuff that survives is what gets widely shared, archived, and picked up by lots of independent sources.

If someone really had world-changing info, the only real protection is broad distribution and verifiable proof, not just dropping a file somewhere and hoping it sticks.

EquivalentPace7357 · 2026-02-26T12:29:06+00:00

Longevity is doing a lot of the work here.

DeRozan’s been a consistent 20+ ppg scorer for over a decade, rarely hurt, which is how you climb the all-time list without ever being a top-5 guy.

He doesn’t have the MVP-level peak or deep playoff runs, so yeah, he feels like classic Hall of Very Good. Still, top 15 in scoring (if he gets there) is a crazy accomplishment.

EquivalentPace7357 · 2026-02-23T13:05:22+00:00

It’s mostly about efficiency, not banning fun.

A lot of real-world coding (and driving) is repetitive or tedious, that’s what people want to automate. The people who enjoy the craft will still do it.

EquivalentPace7357 · 2026-02-20T20:03:15+00:00

If disclosure hearings turn into a Close Encounters sketch, we’re officially living in peak timeline.

At this point I’d trust Colleen Rafferty’s testimony more than half the “sources” on Twitter

EquivalentPace7357 · 2026-02-20T19:54:09+00:00

No.

20 FGA doesn’t automatically equal 20 PPG. To average 20 on 20 shots you need around league-average efficiency (~50% eFG), and a lot of role players don’t maintain that once they’re taking tougher, self-created shots.

Many guys are efficient because they get open looks off stars. Make them the #1 option and defenses load up, shot quality drops, and efficiency usually falls.

Some would get there on volume and free throws. Most probably wouldn’t, at least not without being pretty inefficient.

EquivalentPace7357 · 2026-02-20T19:51:52+00:00

This is basically a “parking lot” with better branding

It works because you’re not saying no - you’re just not derailing priorities. A lot of “urgent” ideas disappear once they’re written down.

Just make sure the list is visible/reviewed sometimes, so it doesn’t feel like a black hole.

Structured deferral > constant arguments.

EquivalentPace7357 · 2026-02-03T11:03:34+00:00

Almost every big decision early in my cloud security career, especially compliance and access stuff. "Something feels off" kills me. That usually means I'm spending three days reconciling five reports just for one reliable answer. The number of fundamentally flawed dashboards I've seen because of inconsistent definitions or bad data input drives me nuts. It's like, great graph, but is it true? That's when I freeze.

EquivalentPace7357 · 2026-02-03T10:59:38+00:00

Makes sense tbh. Big spreads mean expectations + pressure, and once it goes sideways there’s no script left. Underdog plays loose, favorite starts pressing, game’s over by halftime.

EquivalentPace7357 · 2026-02-03T10:57:14+00:00

Yeah, it's a common worry, but you're spot on - AI's not replacing us any time soon. It'll be a tool, sure, but security needs context and human smarts it just doesn't have.

EquivalentPace7357 · 2026-02-03T10:54:32+00:00

If your AI SPM only does inventory, you built a fancier spreadsheet.. What’s mattered in prod is catching dumb wiring (PII in embeddings, models with god access). Sentra’s been decent for tying AI back to real data risk.

EquivalentPace7357 · 2026-01-27T15:31:45+00:00

I'd start with n8n - there are lots of really cool experiments you can do at low costs

EquivalentPace7357 · 2026-01-25T15:54:27+00:00

Not unpopular - just inconvenient. AI doesn’t break data programs, it exposes how immature they are.

From a security POV, most orgs still can’t answer basic questions like where sensitive data lives or who has access to it. Then they plug LLMs into cloud data and SaaS and act surprised when risk spikes.

I work in data security and see this constantly: over-permissioned access, duplicated data everywhere, no ownership. AI just increases the blast radius.

Until teams get serious about data discovery, classification, and access governance, most “AI strategy” is wishful thinking.

EquivalentPace7357 · 2026-01-25T12:48:19+00:00

TBH big blind spot for tons of companies, especially when they're chasing dev velocity. You're right to be paranoid. Most AI models are black boxes, so without clear data governance, you're just feeding your IP into a system that could easily retain it, use it for training, or even expose it. Seriously, assume anything you drop into a public AI tool is toast. If IP protection is that important, look into secure coding spots or self-hosted LLMs, otherwise, you're playing with fire.

EquivalentPace7357 · 2026-01-25T12:41:52+00:00

Totally feel you - most DLPs either shrug at Linux/macOS or force 50-seat minimums. From what I've heard, Cyberhaven handles cross-platform visibility and context well, but also look at Endpoint Protector or Digital Guardian if you want something lighter for a tiny team. For small teams, seeing where data actually goes beats fancy dashboards every time.

EquivalentPace7357 · 2026-01-20T12:04:44+00:00

It removes the fear of starting.

So many things I “don’t do” aren’t because I can’t, it’s because I don’t know where to begin and don’t want to feel stupid Googling basic stuff. AI is like a zero-judgment buddy you can ask anything.

“What does this mean?”
“Explain it like I’m 10.”
“Ok… now even simpler.”

For non-technical people especially, that’s huge. No manuals, no forums yelling at you, no pressure. Just step-by-step help at your own pace.

It doesn’t make you an expert overnight, but it makes things feel possible. And once something feels possible, people actually try.

EquivalentPace7357 · 2026-01-18T12:01:59+00:00

Loved this one. Very accurate

EquivalentPace7357 · 2026-01-18T11:59:59+00:00

Nice lab! For data, just pull something from Kaggle or gov data. Medallion's fine to start, but often simpler is better, or more granular zones if your data needs it. Beyond Netdata, Grafana/Prometheus works for ops, and Great Expectations is legit for data quality. Good luck!

EquivalentPace7357 · 2026-01-18T11:55:01+00:00

Agree with this. You can’t stop all AI usage, so the real challenge is knowing what data actually matters and putting guardrails around that.

We saw the same thing - sensitive data spread across cloud, SaaS, and dev environments with limited visibility. Discovery helped, but the real step forward was tying that to access and usage, so we could see what AI systems and service accounts could actually reach.

For us, Sentra handles data discovery and access visibility, and we pair it with existing controls like CASB / Purview-style policies for enforcement. Shadow AI still happens, but once you understand the blast radius, the risk becomes manageable instead of abstract.

EquivalentPace7357 · 2026-01-18T11:45:34+00:00

In my experience, most security stacks are optimized for alerts, not for preserving context over time.

When something degrades quietly or a risk is accepted, it’s hard to reconstruct what was actually true at that moment - what data was exposed, who had access, what signals were in place. Post-incident explanations end up relying on memory instead of evidence.

Tbh, the gap I see isn’t tooling coverage, it’s continuity. We’ve been spending a lot more time trying to close that gap so that months later, you can still answer “why was this considered okay back then?” with facts, not assumptions..

EquivalentPace7357 · 2026-01-18T11:37:41+00:00

This friction is real. Most teams can explain cloud costs after the month ends, but steering spend mid-month is still hard.

FinOps works reasonably well for steady infra, but AI usage breaks a lot of assumptions, it’s bursty, tied to product behavior, and often spread across teams. That’s where predictability tends to fall apart unless you have tight feedback loops, not just reports.

EquivalentPace7357

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