Started a new role about two weeks ago as a Security Associate at an MSP (I'm remote, working a different timezone than the rest of the team). Coming from a single-org security background, and now I'm dealing with 150+ client environments, compliance frameworks I've never touched before (working toward an MSP-specific certification/audit), and honestly every task I dig into seems to surface three more things I don't have context for.

My manager has been genuinely supportive, low pressure, encourages questions, says things like "we'll figure it out together." But I still feel like I'm constantly hitting walls I can't get past on my own: unclear ownership of tasks, missing documentation, not knowing who to ask for basic org context (who owns what system, where policies live, etc.). I sent my manager an honest note today saying I'm finding it genuinely challenging, which felt like the right call, but I'm second-guessing myself on whether that was too much too soon.

For anyone who's been in a similar spot, new to an MSP, new to a bigger/messier environment than what you came from, how long did it realistically take before things started clicking? Any advice on how to ask for help/context without it looking like I'm not cut out for this? Trying to stay patient with myself but it's rough some days.

Thanks in advance, just needed to hear this gets easier.

During my research into model alignment, I noticed how frequently safety guardrails cause false positive refusals on legitimate cybersecurity tasks.

To address this, I applied directional abliteration to Qwen3-4B. By locating the specific internal activation pattern responsible for refusal and removing it directly from the model weights, I neutralised the refusal response entirely. [model : https://huggingface.co/IamLucif3r/Qwen3-4B-Instruct-2507-Abliterated ]

The process requires no fine tuning, no datasets and preserves the model's original reasoning and coding performance. You can run it using ollama :

ollama run hf.co/IamLucif3r/Qwen3-4B-Instruct-2507-Abliterated:Q4_K_M

What this modification provides:

• Direct, unhindered analysis of exploit payloads, binaries and source code.
• Complete removal of conversational lecturing and moral disclaimers
• Baseline benchmark capabilities intact without catastrophic forgetting.

I have documented the complete methodology, findings & technical setups in my article:
https://blog.anmolsinghyadav.com/llm-abliteration-refusal-guardrails-f227460e2c7c

You can create your own abliterated model.

I've spent a few years doing incident response and negotiation work around real ransomware cases, and "we run tabletop exercises" is the answer I keep hearing from leadership whenever prevention comes up. But I'm curious from people who've actually been in the room, on either side: did it change how your team actually responded when something real happened later, or did it feel more like a compliance checkbox everyone forgot by Monday? What would've made it feel less like theater?

Outside of specific services, typically.

For example, its hard to find a job where you're "just" doing threat hunting. Or you're "just" doing threat intelligence analysis. But rather a lot of these jobs seem to have some kind of overlap.

Its not uncommon for me to be looking at positions for hunting, intelligence, etc. And many times I see the description/skill requirements/duties and they overlap with other positions. The most common trend I see is a position that is basically a Threat hunter + Threat Intel Analyst + Detection Engineer + Forensics rolled into one. Where you're doing hunts, forensics, analyzing intelligence, and also designing your own detection (and testing/maintaining them). This is especially common at companies that don't have huge budgets. So very commonly business side positions.

I've been in the industry for 10+ years. Maybe its my memory, but I don't remember it being this way in the past. Correct me if I'm wrong. But there seems to be a "consolidation" of specializations over the past 3-5 years. Where positions are being rolled into one. Maybe its because of technology advancements (like AI streamlining certain processes). Maybe its because budget constraints. Not sure.

However, I'm curious if its just a bias on my part of what I'm seeing. And its just always been this way.

It’s also described as the asymmetry of cybersecurity/asymmetric advantage of attacker.

Modern security experts often criticize the literal “defender must be perfect, attacker only needs to succeed once” version, because defence in depth means one successful intrusion doesn’t necessarily equal a successful breach.

Long story short, I work in a SOC and its started to take its toll on my personal life and mental health due to burnout. It would be very liberating to just walk away from it all. But I also don't have any other job prospects currently. I have sufficient savings to support me, but the job market is ass so I could be out of work for months and that could also be very stressful. I'm not sure what I should do.

I am completing practice questions based on Security+. I came across this one:

A security auditor discovers that a company has two firewalls of the same model protecting their internal network. What should the auditor recommend for their security architecture?

A. Change one of the firewalls to a different platform

B. Add a firewall of the same model

C. Only use one firewall

D. Remove firewalls in front of the internal network

Regardless of what the Security+ says, do you agree that A is the right answer?

I ask because just about every security professional I have ever met would say C is the right answer, so I am just gathering real-world opinions (while apparently still remembering answer A for the Security+ exam.)

Curious what other security teams and MSSPs are doing to make Microsoft security operations more efficient at scale.

We’re pretty heavily in the Sentinel, Defender XDR, Azure and M365 world, and I’ve been spending more time thinking about the engineering side of running all of this. There’s only so much value in continuing to add detections and playbooks if every new customer, rule, data source, or workflow creates more manual work for the team.

How much are you guys actually automating?
For example, are you deploying Sentinel content through ADO/GitHub pipelines and treating detections as code? Are you automating analytics rules, watchlists, ASIM content, workbooks, Logic Apps, Functions, etc.? If you’re doing detections as code, how are you handling testing, tuning, promotion and customer-specific differences without creating a mess?

I’m also curious how MSSPs/MDRs are handling the improvement side of things. Not just responding to whatever alerts fire, but continuously finding things that could be better in a customer environment. Missing telemetry, gaps in detection coverage, stale rules, bad configurations, unused data, things that could be automated, retention issues, and so on.

Same question around SOAR. Once you get past a handful of Logic Apps, how are you keeping it manageable? Have you built reusable frameworks or common automation, or does it eventually turn into a pile of customer-specific workflows?

And is anyone doing anything genuinely useful with AI internally yet? I’m less interested in giving analysts another chatbot and more interested in things like enrichment, KQL/detection development, case summaries, documentation, reporting, identifying coverage gaps, or helping engineers operate across a lot of environments.

Basically looking for project ideas from people who have already gone down this road. If you had engineering time available and wanted to make a Microsoft-heavy SOC/MSSP noticeably more efficient over the next year, what would you automate or build first?

Also interested in the opposite: things you built that sounded like a great idea but ended up creating more maintenance than they were worth.

How do you decide what defines an excessively encoded URL?

I'm working on a personal project to created a URL parser and detection system and I've hit a wall on how to figure out a way of categorizing excessivness of query encoding?

Here's what I have so far for the function:

def check_query(analysis: URLAnalysis) -> ScanResult | None: """detect excessive encoding entries in URL queries""" og\_query = analysis.query query = re.findall(r"%\[0-9A-Fa-f\]{2}", og\_query) encoded\_count = len(query) 

Many still view logic programming languages like Prolog as purely academic or limited to classic AI. However, in my latest "Hyper Chaos Stress Test (v7.0 God Mode)" benchmarking a custom web application firewall (WAF) engine, this architecture proved to be an absolute powerhouse for high-throughput cyber defense.

When hit with 40,000 Active Hyper Chaos Waves against an intelligence feed database containing 276,717 clauses, the engine maintained a flawless "100% METAL LOCKED SECURE! SHIELD IS IMPENETRABLE" verdict, bypassing exactly 0 unique payloads.

Here is the raw performance data extracted directly from the SWI-Prolog virtual machine execution:

• Total Inferences Executed: 100,201,875 logical reasoning steps inside the main memory.
• Peak Induction Speed: 7,024,009 LIPS (Logical Inferences Per Second).
• Volatile RAM Footprint: ~4.2 MB (4,270 KB allocated, 3,754 KB in use), running ultra-lightweight on a standard 8.0 GB system.
• Garbage Collection Overhead: 7 core and 24 clause garbage collections completed in 0.000 pure seconds.
• JIT Hashing Performance: Scaled automatically to 276,717 JITI Rules across 4,096 memory buckets, locking in an absolute 2,538.4x Speedup Factor.

The Secret? Just-In-Time Indexing (JITI).

Instead of checking criteria sequentially (linear scanning), SWI-Prolog dynamically constructs deep hash tables for the active rule sets. The engine instantly jumps to the exact memory location matching the payload signature, yielding a massive 2,538x speedup.

This proves that declarative logic programming isn't just mathematically sound—it is highly production-viable, offering incredible protection while slashing cloud compute infrastructure costs down to the absolute bare minimum.

check detail hure : https://github.com/lokinpendawa/aethel_core

What are your thoughts on utilizing declarative logic programming for modern, low-footprint cybersecurity perimeters? Let's discuss in the comments below!

Hi,

As I guess is the case with others also, we have a bunch of AI tools available and in implementation phase and everyone is throwing shit into a wall and seeing what sticks.

From a cybersecurity point of view I would like to leech of you guys and hear what are some simple or not so simple implementations of AI you have done within the security/IT teams?

I'll throw as an example of one thing we have done:

Simple agent (Copilot) which has access to all security documentation and to previous answers/discussions which is prompted so it will answer to customer security questionnaires based on our documentation.

Sharing because the framing is useful for anyone trying to understand where Purview ends and third-party tooling begins.

Gartner published 'How to Build a Data Security Program Using Microsoft Purview' (G00856387, September 3, 2026), written for cybersecurity leaders. It covers license evaluation, sensitivity labeling strategy, DLP deployment approaches, and when to supplement Purview with third parties.

The key stat that jumped out: 80% of orgs have E5 or equivalent, but 47% say they are not getting full value from their M365 investment. The report attributes this largely to licensing complexity and governance gaps.

Gartner's central recommendation: treat Purview as a foundation, not an end state. Start with native Microsoft controls. Fill gaps with third-party tools where needed.

They name four vendors in the third-party augmentation section: AvePoint, Syskit, Powell and Rencore, specifically for rapid risk mitigation and enhanced audit capabilities.

What I found interesting is the framing: these tools augment Purview, they do not replace it. The report is explicit that the best approach is layered.

For those managing M365 security programs: the section on evaluating DLP approaches (Enterprise DLP vs Integrated DLP) is particularly useful.

Full disclosure: I work at Rencore, one of the named vendors. Posting for the Gartner framing and stats, which are useful regardless of tool choice.

In a system handling sensitive personal data, suppose a class of trial accounts was deliberately excluded from pre-production access-control testing because those accounts were not intended to access production. After launch, it was discovered that hundreds of those trial accounts were technically capable of accessing all live reporting records.

From a security assurance / C&A / IAM perspective:

1. Would excluding those accounts from effective-access testing normally be considered an assurance scope gap?
2. Is “these accounts were not intended to access production” normally enough reason not to test whether they actually could?
3. Would you expect negative authorisation testing to verify that non-production/trial account types cannot reach production data?
4. Would this normally be characterised as a testing failure, configuration failure, assurance failure, or some combination?
5. What records would you expect to exist afterwards to establish where the control failed?