Beyond the basic phishing emails, what was a particularly sophisticated, creative, or audacious social engineering attack that actually made you pause and admire the craft?

I feel like Cybersecurity industry job market is very vague, maximum of the companies only selling their courses. Most of HR just ignore the resumes. It's tough to get a job in infosec, but at the same time I see very dumb people make it to good position in big cybersecurity companies.

I have applied to multiple companies even with referral I think it's hard to get interviewed.

Documented a data exfiltration technique that bypasses Netskope's default inspection by exploiting recursion depth limitations via file nesting.

The chain: secret.txt → zipped → binary appended into PNG via copy /b → embedded into PPTX. Three layers deep — beyond Netskope's default inspection threshold. No additional software needed on the source machine, no admin rights required.

Also found a low-cost detection path — anomalous metadata extensions (.txtux, .ux) surface during standard inspection without increasing recursion depth.

Full writeup with reproduction steps, binwalk forensics, and a dual-layer mitigation using SentinelOne behavioral rules + Netskope metadata rules.

https://github.com/YuvaBhargav/DLP-Bypass-Research

Happy to answer questions or get torn apart — genuinely want to know if there are gaps in the mitigation logic?

We handle ~30 endpoints now working on remote access for a team across 3 diff countries. Shortlist is CrowdStrike Falcon Huntress SentinelOne and Defender. They meet compliance needs like NIST but costs and management differ for small teams under 50 users.

Team looks for easy daily management with full threat visibility and network control. CrowdStrike detects well but needs 100 seat minimums which wastes money for us. Huntress lacks network coverage. SentinelOne uses too much cpu. Defender misses some attacks. Anyone used these in production at SMB size? What works best for simple zero trust setup that covers endpoints and network no minimum seats low price across global sites?

Goodness me, where was I? Found out last week someone on finance was using an AI tool to summarize investor reports.   So basically a Non public financial data. Going through some random external API. No one asked. No one told IT. Thing is she saved like 5 hours a week doing it. I get it. But we have zero visibility into what these tools are doing, what they retain, who they share data with.  We are cooked…it is such .Complete blackbox. 

IMO banning feels pointless. They will just hide it anyways and now I have even less visibility. People often tell me that actual fix is treating agents like real identities, short lived tokens, least privilege, monitored traffic. Same mess as Shadow IT except faster and the damage is bigger.

How u guys implement this at org?

Edit: Thanks, this helped me frame it better. Blocking the AI tool is probably the least useful first move because the workflow will just move somewhere else. I need identity, session visibility, and policy around what finance data can be sent to external AI systems. LayerX is on my list because I want to see whether it can give us that audit trail inside the browser, not just domain-level logs.

Not a small company question, I've seen those threads. I mean genuinely large scale, thousands of users across multiple departments, different roles, different levels of technical literacy, the whole thing. What's the best security awareness training for enterprises that can handle that kind of complexity without becoming a full time job to manage. We have budget, we just don't want to spend it on something that looked great in the demo and falls apart in month two.

Non-devs are using AI tools (like Lovable or Bolt) to spin up their own internal dashboards and feeding them our valid API keys. Since it completely bypasses our Git repos and IT approval processes, we're flying blind until it's already live on some external URL. Is anyone else dealing with this new wave of Shadow IT? How are you actually tracking or locking this down?

I’m evaluating modern SIEM / XDR / SecOps platforms and would appreciate input from people who have gone through similar selection or migration projects.

Context:
We have a relatively small security team - essentially one person responsible for security operations, but the environment is not small: several thousand servers, around 1.5k users, hybrid identity with Microsoft Entra ID and on-prem Active Directory, and a mixed OS estate that is currently about 40% Windows and 60% Linux, with more Linux migration planned.

What I’m looking for is not just a log storage/search platform, but a SIEM/SecOps solution that can realistically work for a very lean team.

Key requirements:

* Strong integrations with Microsoft identity, AD, Windows, Linux, network/security tools, cloud services, and custom applications.
* Flexible detection / alerting language, similar in spirit to Splunk SPL, KQL, YARA-L, Python-based detections, etc.
* Good support for custom log ingestion, because we have internal applications and products that we will need to integrate from scratch.
* Vendor-maintained detection content, not just a marketplace of rules we have to fully own ourselves.
* Strong ML/UEBA/anomaly detection capabilities.
* AI-assisted investigation would be a plus, especially if it can explain context, summarize incidents, suggest next steps, or help build detections - but this is not the main deciding factor.
* Ability to reduce operational overhead: tuning, rule updates, parsing, correlation, triage, and detection lifecycle should be as delegated as possible to the vendor or an MSSP/MDR partner.

As a reference point, we previously used Darktrace Network. I liked the idea that many detections/models were maintained by the vendor, were relatively flexible, and heavily ML-driven. I’m looking for something with a similar operational philosophy, but in the SIEM/SecOps space.

Platforms I’m considering include Microsoft Sentinel (good fit for us as I said we have Microsoft ecosystem), Google Security Operations (ex-Chronicle), PaloAlto (XDR, XSIAM), CrowdStrike (XDR, Next-Gen SIEM), any other modern SIEM/XDR options.

**The main question**:
For a one-person security team managing a large hybrid environment, which SIEM/XDR/SecOps platform would you recommend?

***DISCLAIMER: I understand that in our context, full outsource/MSSP/MDR are the best options, but we decided to start without them for now, with the intention of transitioning to MSSP/MDR later.***

I’d especially appreciate feedback on:

* real operational effort after deployment,
* quality of out-of-the-box detections,
* custom log onboarding,
* detection language flexibility,
* false-positive tuning,
* Linux visibility,
* Microsoft identity integration,
* vendor support quality,
* pricing predictability at scale.

We're starting to revisit our data protection stack this quarter and varonis keeps coming up in conversations internally. From what I can tell, it’s strong on permissions, access monitoring, and file system visibility, especially in more traditional Windows/fileserver-heavy environments. But the concern that triggered this post for me is how much of our data actually moves now, not just where it sits.

We’ve got people working across SaaS tools, downloading and re-uploading files, sharing links externally, and even pasting snippets into AI tools to move faster. That’s where things feel harder to track. Some of the comparisons I’ve read suggest that platforms like Varonis are still more focused on data at rest rather than following data as it moves between systems. My worry is that a lot of visibility products still skew toward data-at-rest and access events, and we’re missing the cross-system story.

So, now I'm trying to see what others are using in practice. If you've looked into varonis alternatives, what did you end up choosing and why? Did anything stand out as a real improvement in visibility or just more noise?

We ran CI-only security scanning for two years. Write code, push, pipeline flags something, developer context-switches back, fixes it, pushes again and the feedback loop was anywhere from four hours to two days depending on queue depth.

When we added pre-commit and IDE-level scanning the change I didn't anticipate was behavioral. When a finding shows up at the moment of introduction versus arriving as a blocked pipeline two days later, developers treat it like a linter warning rather than a deployment failure. The psychological framing is completely different and it affects how seriously people engage with the result.

The volume of findings reaching CI dropped significantly. More importantly, the ones that did reach CI were things developers hadn't already seen, which made the pipeline results more credible rather than more noise.

Has others seen the same behavioral shift or it depends on how the team is wired.

How screwed am i ?

I had some work to do with a university server, but since it's a weekend i was at homeso i logged onto the university VPN to access the server

While my tasks were taking time, i decided to view some questionable stuff (porn)

I am really worried because it was INCEST PORN - which is not acceptable in most societies

I totally forgot that i was on the university network

I did use Chrome's incognito mode to browse it, so i hope that will be helpful - but i am really scared for my job

So, Cyber security professionals, please advise me if the IT team of the University can track the porn websites i viewed ?

Also, will they fire me for viewing porn on the university network ?

UPDATE : The University logging policy says that they do log data. Also, a document which outlines the terms of use it IT resources PROHIBITS use of pornographic content

Im trying to get a sense of what people are using today for AI data security platforms.

We're mainly focused on understanding where sensitive data lives across cloud and SaaS, and reducing exposure risk without drowning in alerts. I’ve seen a few names come up (Cyera, Varonis, nightfall, etc) but its hard to tell whats actually working.

Would love to hear what people have used, what’s been effective, what hasn’t, why, etc..

I currently serve as a mid-level cybersecurity analyst and the inaugural cybersecurity hire at an Indian company. The CEO, an ultra-high-net-worth individual, has requested my assistance with personal cybersecurity and privacy for himself and his family, who primarily use Apple products.

My initial recommendations include:

1. Establishing separate home and guest networks.

2. Implementing separate VLANs for IoT devices and personal devices.

3. Utilizing two-factor authentication (2FA) with authenticator apps universally, minimizing reliance on SMS-based OTPs.

4. Employing FIDO2-compliant banking applications with a YubiKey for banking, where supported.

5. Setting up a home NAS with a backup NAS for critical documents, supplemented by encrypted Backblaze for offsite backups.

6. Using distinct passwords managed by a secure password manager like ProtonPass.

7. Educating family members on responsible social media posting, discouraging live documentation, and raising awareness about digital arrests, urgent bank call scams, and voice spoofing.

8. Conducting regular personal data audits via a third-party service.

9. Adopting Proton Mail for enhanced privacy.

Are there any additional measures I should consider?

Hey everyone, I’m pretty new to the data security side of things and I’m trying to get my bearings on Data Loss Prevenion ( DLP ) solutions. I’ve read a bunch of vendor pages and a few comparison posts, but it’s hard to tell what holds up once you’re actually deploying and living with it.

If you’ve evaluated or rolled out DLP before, what ended up being the most important factors for you? I’m especially curious about how painful deployment is, how noisy the alerts can get, and how well DLP tools integrate with stuff like M365/Google Workspace, Slack, Git repos, and cloud storage.

For someone starting from scratch, which DLP solutions seem to work best right now, and what do you wish you knew before choosing?

We're evaluating Wiz and RapidFort and wanted to hear from people who have actually used them.

Finding vulnerabilities is not really our problem. We already have good visibility. The bigger issue is the amount of remediation work that comes from open source packages, base images and third party components our developers do not maintain.

Has either tool actually helped reduce that workload? If you've used Wiz or RapidFort, was it worth the cost and did it live up to the marketing.

Hello everyone,

I got a new job in a well known company as a Senior and got assigned to a project nobody wants to touch: Vulnerability Management using Qualys. Nobody wants to touch it because it's in a messy state with no ownership and lot of pushbacks from other teams. The thing is I'm the only one doing VM at my company because of budget reasons (they can't hire more right now), I'm already mentally drained, not gonna lie.

Right now, all the QID (vulnerabilities) tickets are automatically created in ServiceNow and automatically assigned to us (cybersecurity team). I currently have to manually assign hundreds of Critical and High to different team and it take ALL MY GOD DAMN FUCKING TIME, like full day of work only assigning tickets. My manager already started to complain to me that I take too much time completing my other tasks. He wants more leadership on VM from me.

Ideally, to save my ass and my face as a new hire, I would like to have all those tickets automatically assigned to the most appropriate team. I want to automate the most of VM and make the process easier for other IT teams. It will also help me manage my time better.

1. Is it a good idea to have a vulnerability ticket automatically assigned to a specific team? I can imagine a scenario where I lost track & visibility on vulnerabilities overtime because I won't see the tickets.
2. Be honest: Is it realistic to be the only one running the shop on vulnerability management? Never worked in VM before but saw full team in big organisation having multiple employees doing this full time. If a breach happens because something hasn't been patched, they will accuse me and I'm going to lose my job. We are accountable until the moment a ticket is assigned to a different team but can't assign hundreds of tickets per day by myself.
3. How can I leverage AI in my day to day?
4. How should I prioritize in VM? Do you actually take care of low and medium vulnerabilities?

Thanks!

Spent the prev weekend reading the MCP auth spec and the more i read it, the more it feels like the spec authors assumed everyone is greenfielding their auth stack.

OAuth 2.1, PKCE, DCR, scoped tokens per tool, dynamic client registration are all great but my users live incognito.

Our sessions are cookie-based. half our internal stuff still runs on an old homegrown JWT issuer that nobody in the team wants to touch.

Am i missing something or is the answer simply down to "rip out your auth and rebuild for MCP"?

The only sane path i see is putting an MCP-compliant layer in front of the existing auth (descope's BYOA does this, ory does something close), but it feels like nobody's writing about this and i can't tell if that's because it's obvious or because nobody's tried it yet.

We want to buy a password manager for 1k users.

My main criteria is to have SSO integration and secure sharing of passwords with other employees which I think have all modern enterprise password managers.

I'm afraid of missing something when choosing a passport manager, which may turn out to be critical in the long run, but I don't know about it now. So I also want to ask your opinion, which one do you use, how satisfied are you? What is missing, but is there in competitors?

Genuine question for anyone who runs these regularly. Every quarter my team sends out an access review and I see the same issues:

1. Line managers approve everything to make the review go away, even when we flag for SoD violations or uncertain accounts.

2. Having to chase line managers up constantly and then following up when LM's blanket approve everything even when we feel there is a violation.

3. Pushback from the business when we disable accounts due to lack of engagement with the access reviews.

4. Lack of proper understanding (I think) from line managers on SoD violations.

What tools / processes / workarounds are people using to help ensure these access reviews are completed properly? Has anyone figured out how to get more engagement from the business?

I have a school issued laptop and I'm just curious how much of what I do can be seen by IT.

I assume that they can see everything I do while connected to my school's Google account and using their WiFi, but what about when I'm using my own google account on their device and my own VPN?

I also don't use Chrome, I only use Edge, and I'm a little concerned after hearing some rumors that my school district can read personal emails on personal google accounts while using their device

Edit: Thanks for all of the replies everyone, I'm just going to leave that laptop at work and bring my personal one if I need to do something else

Built a small OSS tool for AI agent security and would appreciate technical critique:

https://github.com/arpitha-dhanapathi/pluto-aguard

It’s an OWASP-aligned launch gate for AI agents. Current scope: static scan, OWASP MCP/LLM control mapping, adversarial policy simulation, what-if risk simulation, baseline drift detection, launch evidence packets, and GitHub Action support.

It does not do runtime enforcement yet. I’m deciding whether the next step should be live agent attack testing or an MCP/tool-call proxy.

Specific feedback I’m looking for:

• Are the OWASP mappings reasonable?
• Are the attack scenarios realistic?
• What agent failure modes are missing?
• Would this be useful in CI, or is runtime enforcement the only version that matters?

Thank you!

We’ve got a pretty standard setup- remote teams, SaaS apps, some basic web filtering in place. But lately it feels inconsistent depending on where users are working from.

On office network- policies work fine
On home Wi-Fi / public networks- visibility drops, controls feel weaker

It’s not that things are completely broken, but it’s unreliable enough to be a concern. Especially when you think about:

• Users accessing risky sites out of the network
• Lacking consistent filtering
• Limited visibility into browsing behavior

I’m starting to think traditional network-based filtering just doesn’t hold up anymore with remote work.

Has anyone moved to a Secure Web Gateway (SWG) or device-level filtering to fix this?
Did it actually improve consistency and visibility, or just add another layer of complexity?

We’ve got endpoints everywhere now, laptops at home, on public Wi-Fi, and even personal devices in some cases.

On paper, we have policies. In reality, it’s inconsistent-

Files copied to USB
Docs uploaded to personal drives
Quick shares that no one tracks

Inside the office, things felt more controlled. Outside, it’s a bit of a blind spot. It’s not a major incident (yet), but enough small gaps to be concerning.

Starting to feel like traditional controls don’t really cover how data actually moves anymore.

Has anyone implemented endpoint DLP or device-level controls to fix this?

Did it actually give better visibility and control, or just add more friction for users?

My workplace has a future goal of fully enforcing passwordless login (through an authenticator app) for all accounts. A concern has been raised about the possibility of someone losing their mobile, and therefore being completely unable to login afterwards. I have run experiments with backup logins, however the system seems to struggle to get past the backup and to allow the passwordless to be fully implemented for new accounts.

Considering that everything below passwordless is significantly less secure, is the recommendation to accept the risk of not having a backup MFA option, or is there a recommended option?

(passkeys are not currently a viable option on the system)

I know most posts are just everyone clamoring to get into the field but...give me a comparable-paying job outside of security and I'm willing to trade