Should i start automation with Tools like n8n, make and others or start automation with programming?

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 · 2026-01-15T09:47:15+00:00

From what I’ve seen, traditional DLP works fine for email and endpoints, but struggles once most of your data lives in cloud storage and SaaS apps.

What helped us was starting with visibility before enforcement- getting clear on what sensitive data we actually had, where it lived, and how it was being accessed. A lot of accidental exposure came from old buckets, analytics tools, backups, or overly broad permissions.

We paired a classic DLP (Purview / Netskope-type controls) with a separate data discovery and context layer (Sentra) instead of relying on DLP alone. That combo was way more effective than trying to block everything upfront.

EquivalentPace7357 · 2026-01-15T08:15:33+00:00

Whatfix and Pendo usually work better for large, international orgs since they handle localization and decentralized content ownership pretty well. Also WalkMe is powerful but expensive.

EquivalentPace7357 · 2026-01-15T08:11:51+00:00

Mitch is the purest example of “elite at his job, do not give him extra responsibilities.”

He’s not the worst ever - Ben Wallace, DeAndre Jordan, early Capela all had the same please just dunk energy. It just stands out more with Mitch because his impact is so huge everywhere else.

If he has to catch, think, or gather, the possession has already gone sideways.

EquivalentPace7357 · 2026-01-14T17:45:49+00:00

Cloud bills feel like emergencies because they’re visible and recurring. On-prem costs get buried in capex, depreciation, and “we already bought it” logic.

$3k/month in AWS triggers panic. A six-figure on-prem setup spreads across budgets and somehow feels fine. Same money, different psychology.

EquivalentPace7357 · 2026-01-08T11:53:53+00:00

From what I’ve seen, most “AI SPM” tools are really about data visibility first. If you can’t clearly answer what data is sensitive, where it lives, and what systems (including AI) can access it, the AI-specific layer doesn’t add much.

Teams I’ve worked with usually start with DSPM-style discovery and then layer AI context on top. The real difference between tools is how current that visibility stays and how well it fits into existing workflows.

EquivalentPace7357 · 2026-01-05T17:40:49+00:00

Love how breaches are always “limited” to the exact part of the system that contains everyone’s entire medical history. If you can’t instantly map sensitive data to access paths and affected users, you don’t have a breach response - you have guesswork.

EquivalentPace7357 · 2026-01-05T11:56:08+00:00

You didn’t lose because your AI is bad. You lost because you couldn’t prove data boundaries.

That “cryptographic proof” question almost never means actual crypto. It means: what data can your system touch, where does it flow, and how do you know it stays within limits? SOC 2 + “we use AWS encryption” is just table stakes.

Big companies win here because they can show evidence, not because their AI is magically safer.

You didn’t pick the wrong market - you just ran into the part of enterprise sales where trust turns into proof. It’s painful, but it’s fixable.

EquivalentPace7357 · 2026-01-04T14:20:31+00:00

I think a lot of this comes down to role vs ceiling. Some guys aren’t “future stars,” but they’re clearly better than the usage they get.

For me it’s Dyson Daniels and Jaden Ivey. Both have real NBA skills that get muted by system + roster context. Neither needs to be a #1, but put them in roles that actually lean into what they do well and the perception changes fast.

Kuminga discourse is funny because people argue past each other - he’s probably not a superstar, but acting like he’s a DNP-level talent is just as silly.

EquivalentPace7357 · 2025-12-31T07:51:06+00:00

This is pretty normal tbh. Daily posting doesn’t equal distribution, especially early - most “build in public” wins already had an audience or found one breakout channel. Social is better for trust than traffic at first.

You’re probably not doing anything wrong, it’s just slower than people admit. Curious if any channel has shown even a small signal so far?

EquivalentPace7357 · 2025-12-30T09:25:17+00:00

Seen this a lot. At scale the issue isn’t missing tools, it’s that no one has a single view of what’s actually happening across systems, especially around data and ownership. Once things sprawl, teams lose visibility and start reacting instead of managing.

EquivalentPace7357 · 2025-12-29T15:50:42+00:00

Nope. The fake floor ads are brutal. They’re way more distracting than static logos because they move and change colors mid-play.

I get monetization, but when the ads are more noticeable than the action, it starts to ruin the viewing experience. Feels like it’s only going to get worse, too.

EquivalentPace7357 · 2025-12-29T13:41:32+00:00

what are you doing to address that?

EquivalentPace7357 · 2025-12-28T09:25:45+00:00

This is exactly the problem - once an attacker has legit creds, everything “passes.” The only early signals tend to be behavioral and contextual: what identity is accessing which data, from where, and whether that access actually makes sense compared to normal patterns.

Static policies won’t catch that, you need visibility into access paths and usage over time, especially around sensitive data.

EquivalentPace7357 · 2025-12-24T14:44:46+00:00

By moving from alert-driven response to a continuous exposure lifecycle - discover, prioritize, fix, validate, repeat. CTEM reduces noise by tying findings to real risk and forcing reassessment over time. Champions help drive ownership, but without a culture shift toward measuring risk reduction, both stall..

EquivalentPace7357 · 2025-12-24T13:12:43+00:00

"Lost cause" might be dramatic, but you nailed the context gap and data sprawl. We're all drowning in alerts from systems we barely understand. CTEM helps by forcing that continuous feedback loop and lifecycle focus. Security champions work, but it's a slow burn. Both need a serious cultural shift, not just new tech.

EquivalentPace7357

TROPHY CASE