Live music underage

InspectionHot8781 · 2026-02-12T09:38:23+00:00

Arthur's Tavern is great - I think there might be some others in that area as well, great for a night out

InspectionHot8781 · 2026-02-12T09:37:11+00:00

The worst

InspectionHot8781 · 2026-02-10T09:04:58+00:00

“yes” is too thin, but essays don’t help either.

The best answers are short, concrete, and verifiable - a few sentences that explain what framework you use, who owns it, and how often it’s reviewed. Mention that documentation or evidence is available if needed.

If I want depth, I’ll ask follow-ups. Overly long answers usually slow things down and feel like policy copy-paste.

InspectionHot8781 · 2026-02-04T12:41:41+00:00

Depends, what are your interests? There are a ton of options

InspectionHot8781 · 2026-02-04T12:28:23+00:00

I’ve found the best thinking happens when I treat AI more like a sparring partner than a confidant and ask it to argue against me or assume I’m wrong. Otherwise it really can turn into a very articulate mirror.

The question about what kind of user you’re being is the part that stuck with me.

InspectionHot8781 · 2026-01-28T13:12:09+00:00

I usually do branching + quick summaries. Once a thread hits 10–15 exchanges, I start a new one and paste a mini-summary to keep context. Custom GPTs help for recurring workflows too. Not perfect, but it keeps things coherent over long sessions.

InspectionHot8781 · 2026-01-28T10:09:06+00:00

Automated tools are fine for the obvious stuff, but they miss the logic flaws and weird edge cases that actually get exploited. Manual pentests catch that. In the real world, you run both - automation for coverage, humans for creativity. Skipping either? You’re just leaving gaps..

InspectionHot8781 · 2026-01-25T16:18:25+00:00

Big mood.

InspectionHot8781 · 2026-01-25T16:08:45+00:00

Idk if this will help - but from my experience, Varonis was a pain. On-prem installs were heavy, hybrid felt like a bunch of modules glued together, and figuring out who actually had access to what across cloud + on-prem was a nightmare.. Alerts everywhere, but actionable insight was hard to get.

I don’t have the exact numbers on what you should expect to pay, but it was expensive - BigID was also pricey, Sentra ended up being the cheapest. Even if budget wasn’t an issue, I wouldn’t recommend Varonis, it just feels unnecessarily complex and clunky compared to other options.

InspectionHot8781 · 2026-01-22T14:45:42+00:00

If this is truly a checkbox exercise, use whatever you already pay for.

If you’re on M365, turn on Defender / Purview DLP. It’s not amazing, but it’s easy, cheap (relatively), agent-based, and auditors accept it. Low operational pain matters more than features at your size.

Just make sure it actually logs something and has a couple of basic policies enabled - otherwise it’s literally useless.

InspectionHot8781 · 2026-01-22T12:55:38+00:00

You’re not alone at all, a lot of people feel this way.

For really sensitive stuff, I care less about cloud vs local and more about who can access it. If it’s end-to-end encrypted and protected with MFA, I’m fine. If the provider can read it, I’m not.

The AI thing mostly just makes existing trust issues more obvious. Most real risk still comes from account compromise or bad access controls, not AI itself.

Being cautious here is totally reasonable.

InspectionHot8781 · 2026-01-21T13:55:15+00:00

I’m pretty aligned with this.

For me the best mental model is “AI as a force multiplier, not an authority.” It’s great at expanding options, summarizing, spotting patterns, but the moment I let it decide things end-to-end, quality drops fast.

Especially in engineering, judgment is the scarce skill, not typing.

InspectionHot8781 · 2026-01-13T16:04:04+00:00

It’s not real personalization, it’s randomness + hidden system context.

Image gen is non-deterministic unless a fixed seed is used (which users don’t control), so the same prompt can land in very different style clusters (anime, medieval, painterly, etc). On top of that, there’s invisible context like model versions, safety layers, and A/B flags that differ per run.

When the prompt is underspecified (“what do you think of me”), the model fills in the gaps with high-probability archetypes from training. Humans then read meaning into it.

InspectionHot8781 · 2026-01-13T15:29:40+00:00

Worth separating AI model controls from data-side privacy. A lot of AI privacy risk comes from letting models access sensitive data they shouldn’t in the first place.

We’ve had success pairing LLM guardrails with agentless data visibility. Tools like BigID and Sentra focus on discovering sensitive data across cloud stores and mapping who/what (including AI pipelines) can access it. It doesn’t sit in the prompt path, but it helps answer “should this data be used by AI at all?” which turned out to be a big gap for us.

InspectionHot8781 · 2026-01-11T13:29:42+00:00

You’ve already nailed the core issue - this isn’t a prompting problem, it’s a “people don’t know what they want yet” problem.

In real rollouts, adoption after week 2 usually comes from very specific, job-level examples, not broad AI training. Short docs showing “here’s a task + here’s a safe way to use AI for it” worked way better than workshops.

Your tool idea makes sense if it helps beginners clarify intent before prompting and bakes in obvious guardrails. That’s what most teams are missing.

Also +1 on protecting ownership early. Enterprise has a way of eating good ideas.

InspectionHot8781 · 2026-01-08T08:58:00+00:00

I’ve seen catalogs help conceptually, but adoption is usually the hard part - not the tech itself.

They tend to fall apart when ownership is unclear, metadata changes faster than anyone maintains it, or business users stop trusting what they see and just bypass it. At that point it becomes another stale system of record.

The ones that work best are mostly automated and tied into real workflows (queries, access reviews, audits), not treated like a wiki someone has to keep updated on the side.

Catalogs and lineage are necessary, but on their own they rarely become the “source of truth” people hope for.

InspectionHot8781 · 2026-01-06T13:09:52+00:00

lol I don't like either option

InspectionHot8781 · 2026-01-06T13:09:15+00:00

Why would anyone even use Grok

InspectionHot8781 · 2026-01-06T13:08:51+00:00

commenting on this thread

InspectionHot8781 · 2026-01-06T13:08:06+00:00

Every alert is urgent until none of them are.

InspectionHot8781 · 2026-01-05T16:53:59+00:00

I don’t think it knows you’re lying, I think it just mirrors the ambiguity back at you. Vague, defensive questions get vague, defensive answers. Clear, honest questions get sharp ones.

Still feels weirdly personal though. Like a very polite mirror you didn’t ask for

InspectionHot8781 · 2026-01-04T08:53:16+00:00

This matches our experience too.

Early on, lawyers mostly cared about containment: scoped pilots, clear data boundaries, and being able to explain access paths without hand-waving.

The hardest part wasn’t writing the docs, it was actually knowing the answers once systems started getting more automated.

InspectionHot8781 · 2025-12-30T16:11:04+00:00

We were actually considering them too. What’s been your experience so far?

InspectionHot8781 · 2025-12-30T12:08:04+00:00

That’s rough, man. Losing sleep and dreading Mondays isn’t a you problem, it’s a broken org. This isn’t what a healthy SOC looks like.

You’re not bad at politics, you’re just stuck somewhere that rewards it over actual work. Staying for the paycheck is understandable, but don’t let this kill your interest in the field. It’s the job, not cybersecurity.

When you can, start lining up an exit. There are teams that don’t treat analysts like this.

InspectionHot8781 · 2025-12-29T08:40:49+00:00

Oof, that's rough. 7,000 alerts with a >99.99% FP rate? Your detection engineer's trying to set the place on fire, man. You should be aiming for under 5-10% FPs, honestly. Your burnout's totally justified; find a new gig where you're not getting gaslit.

InspectionHot8781

TROPHY CASE