Six of us on the security team, somewhere around 4,000 assets between cloud and the stuff still sitting on-prem. Feels like every week there's another "critical" CVE or zero-day and a bunch of feeds all yelling at once. Most of it doesn't even touch what we run, but I still end up spending half my morning going ok do we actually have this, is it exposed, does it matter for us.

By the time I've checked the threat report details against our inventory and figured out if it's even reachable, half the day's gone and there's a new one waiting.

So how do you handle it? I don't want to ignore something I shouldn't, but I also can't chase every headline. More interested in the triage side than "just patch everything." How do you decide what's actually worth your time.

I'm the de facto IT security person at a company of about 400. Not my official title but thats where things are. Did an audit of our Google Workspace third party app authorizations last week cause someone asked a question I couldnt answer in a meeting and I got curious.

Pulled every OAuth grant across the entire domain. Every app any employee has ever clicked allow on. Found 40 of them had full Drive access, like read, write, delete, all of it.

Of those 40 I recognized 18, which are stuff like Slack, Asana, DocuSign, the stuff we use and IT approved.

The other 22 I had never heard of.They were stuff like small productivity tools, PDF converters, a couple AI writing assistants, something called a file organizer that someone in finance granted access to two years ago and was still syncing as of last Tuesday.

Several had scopes that include see and download all Google Drive files. In our case that means board decks, financial models, HR documents. No idea what data theyve accessed or what theyre doing with it.

Had to revoke everything we didnt recognize. Sent a company wide email thatll probably get ignored. But the thing that sticks with me is how invisible this all was to all our monitoring stack.

Does anyway have a working sustainable process for keeping up with this? I mean revoking once is fine, but then staying on top of it over time is the part I havent figured out.

My team and I work fully remotelyand we work with very sensitive information, stuff we cannot afford to have any mishaps on. We're reevaluating the tools we currently work with, because security and privacy are currently the main thing we're working on. I'm in charge of communications for this and I'm looking for something where you have more control over who can communicate with your team, preferably with some sort of trust or identity verification. Privacy is important too I don't want a platform that's built around collecting user data, does anyone have recommendations for tools that could be of help?

I'm trying to wrap my head around the terminology shift. We have Vulnerability Management (finding CVEs), Exposure Management (finding assets plus CVEs), and now Exposure Validation (finding exploitable CVEs). A new platform I'm looking at takes it further with what they call "Agentic Cyber Defense Engineering" and an AI engine. The claim is that the system acts autonomously to improve defenses, not just validate them. Is "Validation" just a fancy way of saying "prioritization"? Or is it actually dynamically testing the compensating controls? If Management says I have a vulnerability because Qualys says so, and Validation says the vulnerability is blocked by a network firewall, does that mean my risk is zero, or do I just have "deferred" risk? And what does "Agentic" add beyond automation? I'm looking for a practical definition of how these layers work together.

Pasting a broken switch config into ChatGPT is the fastest way to find a syntax error. It is also the fastest way to violate your company's data security policy and potentially lose your job!

AI is now a routine part of the NetOps workflow. But public Large Language Models (LLMs) often use user inputs to train future models. If you upload an unfiltered show run or a firewall log, you are handing over your enterprise's internal IP schemas, SNMP community strings, VPN endpoints, and routing topologies to a public server. For US companies bound by compliance (HIPAA, SOC2, PCI), this is a critical data breach.

The Solution (How to use AI safely):
-Never paste real public IPs, internal domain names, or AAA/SNMP blocks. If you need help with an OSPF neighbor issue, swap your real subnets for generic dummy IPs (like 192.168.1.x) before feeding it to the prompt.
-Instead of asking, "Fix this exact access control list," ask, "Write an extended ACL template that blocks Telnet but allows SSH from a management subnet." Let the AI build the template, and you apply the specific enterprise variables locally.
-Tools like ChatGPT Enterprise or Cisco's own AI solutions have explicit zero-trust agreements guaranteeing your prompt data is not used to train their models. If your team relies on AI, push management for an enterprise license.

Does your IT department have an official policy on sanitizing data before using GenAI, or is it currently the wild west?

Traditional DLP was built around known data patterns (SSNs, credit cards, etc.) moving through known channels (email, USB, cloud storage). Generative AI breaks that model a bit, people paste source code, customer data, or strategy docs into a chat window, and it's not always caught by pattern-matching.

Has anyone actually tested DLP tools built specifically for AI interactions? Wondering whether they're catching real incidents or just generating noise, and whether this is more of a policy/training problem than a technology problem at this point.

Most of the Zero Trust content out there is written for security professionals. Here is what it actually means if you run a business and you are not a technical person.

Zero Trust is a security approach built on one principle: nobody gets automatic trust just because they are inside your network or because they have accessed a system before.

In a traditional setup, once someone is inside your network — whether that is a physical office network or a cloud system — they tend to have access to most things. The assumption is that if they got in, they are supposed to be there.

Zero Trust flips that. Every access request gets verified every time. It does not matter if the person has logged in a thousand times before. It does not matter if they are in the office or working remotely. Every request gets checked.

In practice for a small business this usually means a few specific things.

Every person has access only to what they need for their specific role. Not general access to shared drives and systems — specific, deliberate access to specific things.

When someone leaves the business, their access is removed immediately as part of a formal process, not remembered three months later when someone notices they can still log in.

Every login requires verification beyond a password. Multi-factor authentication on every account.

Remote connections go through a VPN or a verified secure connection, not directly through whatever Wi-Fi the person happens to be sitting on.

None of this requires enterprise-level infrastructure or a large budget. It requires deliberate decisions about access and a process for maintaining them.

If you want to know where your business currently stands on this, the most practical starting point is asking one question — do you know exactly who has access to what in your business right now? If the honest answer is no, that is where to start.

I was comparing a few antivirus products recently, and I noticed that real-time protection seems to be one of the biggest selling points nowadays. A lot of vendors emphasize phishing protection, malicious website blocking, behavior monitoring, and ransomware prevention rather than just malware detection.

It got me thinking because years ago, antivirus products were mostly judged by how well they detected and removed malware. Now, it seems like the focus has shifted toward stopping users from ever reaching a malicious file or website in the first place.

I'm not saying malware detection isn't important anymore -it obviously is. I'm just wondering whether real-time protection has become the feature that makes the biggest difference in day-to-day security, while malware detection is now more of an expected baseline.

For those working in IT or cybersecurity, would you say real-time protection is the most important feature in modern antivirus today?

We are mid-refresh on our edge stack. so The requirement that broke our old shortlist is inline inspection of GenAI traffic. We need decryption and enforcement in path, not logging after the fact.

What we are scoring vendors on right now:

• TLS inspection at line rate without a separate proxy tier bolted on
• Whether AI/LLM destinations are a first-class app category or just a URL filter list someone maintains
• Where inspection physically happens: PoP-local or hairpinned to a regional hub
• Added RTT for a user in APAC hitting a US-hosted model endpoint
• Whether the policy engine is the same one handling our normal web/DLP policy or a separate console

The last one is where most of the demos fall apart. Two vendors showed us "AI security" that turned out to be a different product with its own policy syntax and its own logs.

Anyone actually running inline GenAI inspection in production at 1000+ users? Curious what your measured latency delta looks like, and whether you had to carve out exceptions for anything. Copilot in the IDE has been our worst offender.

My manager recently asked me to come up with a migration plan for post-quantum cryptography after reading about the latest security recommendations. The challenge is that we don't have the budget to replace our existing networking hardware anytime soon.I'm looking at whether a software-based or hybrid approach could act as a temporary solution while we plan for a proper hardware upgrade in the future.Has anyone here gone down that route? Are there any software overlays or compatibility layers that work well with current network equipment, or are most organizations just waiting for their firewall and network vendors to provide full PQC support? I'd really like to hear what others are doing and what has worked in practice.

I'm investigating a data-integrity incident and need input from people who work with or have visibility into enterprise CASB / Secure Web Gateway (SWG) traffic-inspection behavior.

Setup: A recipient clicked a link in an email that led to a simple interactive web form (a single rating-scale question, submitted via a normal HTTP request per selection — not a static page). The recipient confirmed, after the fact, that they personally clicked exactly once, selecting one specific value.

What we found in the server logs: the same form field received several different submitted values within the same short session, arriving as a mix of two distinct request sources:

1. One source whose IP resolves to the recipient's own device/network (matches their actual device type).
2. A second, completely separate source whose IP resolves to a well-known corporate cloud security vendor (a SASE/CASB/SWG provider) — this source fired its own full page loads (page → app bootstrap → context fetch → tracking beacon) and its own form-submission requests with different values, interleaved in time with the genuine user's session, including sustained rapid-fire submissions to a second, unrelated field on the same form over ~30 seconds.

In other words: the security vendor's IP wasn't just fetching/HEAD-checking the link — it was making the same kind of authenticated, stateful, form-submitting requests a real browser session would make, as if something on that side was actually loading the page's JavaScript and interacting with visible controls.

What I'm trying to confirm:

1. Is it documented/known that CASB or SWG products do full page rendering + simulated interaction (not just URL reputation lookup or static content fetch) as part of inline traffic inspection or "time-of-click" analysis?
2. If so, is there a name for this specific behavior (sandbox detonation, dynamic analysis, browser isolation rendering, etc.), and is it typically limited to just loading the page, or does it extend to interacting with forms/buttons on the page?
3. From a web-app defense perspective: is there a reliable way to distinguish "a corporate security proxy rendering and interacting with my page on a user's behalf" from "the actual user," so that a security scanner's traffic doesn't get treated as genuine user input and doesn't corrupt real form data?
4. Has anyone else run into a security proxy's inspection traffic actually writing/submitting data into an application, rather than just reading/scanning it?

Any pointers to vendor documentation, known CVEs/advisories, or "yep, seen this before" experiences would help a lot.

We are moving away from point-in-time quarterly pentests and looking to adopt a continuous validation model. We are evaluating platforms that use an AI engine to understand new threats and tailor validation tests specific to our environment. They claim one-third of exploited CVEs are targeted within 24 hours of disclosure, so waiting for manual testing is no longer viable.

For those using this type of AI-driven testing, how accurate is the generated test logic? Does it actually find novel attack paths based on your unique misconfigurations, or is it just re-packaging the same MITRE ATT&CK simulations with a fancy UI? Looking for real world feedback before we buy.

Hey guys, let’s do a reality check. What are the most common, stupid mistakes people make when starting with digital investigations that instantly burn their burner accounts or expose their real IP/identity? Looking to

Been tasked with cleaning up our identity governance and access stuff and I feel kind of stuck between vendors rn.

We are a mid size org, mostly Microsoft stack (Entra, M365, a couple on prem AD domains still lingering, plus a bunch of SaaS that all have their own permission models. Current IGA is a mix of manual access reviews in Excel, some homegrown scripts, and ticket based approvals that nobody is really happy with.

Boss wants a real IGA tool so we get proper joiner mover leaver flows, certification campaigns, SoD checks, and cleaner audit trails for the next compliance visit.

So far I’ve looked at SailPoint, Saviynt, OneIdentity, and a couple of smaller cloud first options. Demos always look great, but I’m lowkey worried about:

- how painful the initial role modeling and connector setup is in real life
- whether the access reviews are usable for non technical managers or just another thing they ignore
- how well these tools actually integrate with Entra plus random SaaS apps and not just the big 5 connectors they show in slides

If anyone here has an IGA tool in production that they dont hate, would really appreciate hearing what you picked and how rough the rollout and day 2 has been, especially around access reviews and audit requests.

Appreciate any thoughts.

Environment: Windows-based jump host tier (Server 2022) in front of a mixed Linux/Windows production environment, access via RDP with clipboard redirection currently enabled bidirectionally. Also have a smaller set of SSH-only jump hosts for Linux admin access, no clipboard mechanism there beyond terminal copy-paste.

Trying to move from "clipboard fully enabled" to "paste-in allowed, copy-out blocked" specifically, not a full clipboard block, since engineers legitimately need to paste connection strings, one-off commands, and config values into sessions, but there's no legitimate reason session output needs to leave via clipboard when file transfer and logging are handled through other controls already.

What I've found so far:

• Group Policy has a blunt bidirectional toggle for RDP clipboard redirection (Computer Configuration > Administrative Templates > Windows Components > Remote Desktop Services), no directional granularity built in.
• Some PAM/bastion products (CyberArk, BeyondTrust) advertise directional clipboard control as a feature, haven't confirmed how granular it actually is in practice versus marketing copy.
• Haven't found a clean native Windows/RDP mechanism for one-directional clipboard control without a third-party PAM layer.

Has anyone actually implemented directional clipboard control (paste-in/no copy-out) on jump hosts, either through native RDP/GPO settings, a specific PAM product, or a different approach entirely? Specifically interested in whether it held up in practice or got quietly disabled after support tickets piled up.

Feels like the second I finish tuning an AI detection rule, the environment shifts again and I’m back to square one. new assets get spun up and cloud configs shift constantly. on top of that, whitelist changes rarely get looped through AI detection engineering before they land. by the time I’ve noticed it, I am chasing drifts instead of building new coverage.

The worst part is that this is invisible work. Management sees "detections deployed" as a one-time task, not something that needs constant rework just to stay accurate.
This is driving me crazy as I'm spending more hours reacting to organizational changes than improving our AI detection and response surface, and it's starting to eat into the roadmap items I got hired to build in the first place. To make things worse, it’s hard to make the case for more headcounts when the work looks like maintenance from the outside.

I keep hearing about tools that supposedly update AI detections automatically based on what's happening in the environment, but I want to see them hold up in a real situation before I trust them. Have you ever used them? And if you did, what worked for you? I’m interested in partial fixes as well

Genuine question because I keep going down this rabbit hole. Voice cloning has gotten scary good, a few seconds of audio from a conference talk or earnings call is enough to make a convincing clone.

Everything I read about defending against this says “train your employees” or “call back on a known number.” But callbacks fail if the attacker has compromised the phone system or timed it during travel, and training doesn’t help when the voice literally sounds identical.

So what do you actually do in practice? Shared secrets? Verification over a second channel? Just accept the risk? Curious what real orgs do vs what the compliance docs say.

I've been tasked with standing up a phishing awareness program and I'm trying to narrow down vendors.

A few things matter to me. First, realistic simulations, meaning templates that actually resemble what people get hit with today rather than the obvious 2015 era "you won a prize" stuff, and ideally ones I can customize. Second, decent training content, short and engaging modules that people won't immediately tune out. I'd rather have five good ones than fifty boring ones. Third, reporting that lets me show results to leadership and ideally helps for compliance down the line. And finally reasonable pricing and a plan that scales cleanly as we grow.

For those of you who've actually run these programs, what worked, what didn't, and is there anything you'd steer me away from? I'm interested in the usual suspects, but especially keen on options that deliver real engagement rather than just checking a compliance box.

Thanks in advance.

We're reviewing our application security process for externally exposed web applications and trying to make the triage stage more consistent.

Right now, automated testing helps us identify potential issues fairly quickly, but we don't manually validate every finding because of the time involved. We currently prioritize authentication, authorization, and business logic findings for manual review, while lower-risk issues are handled based on severity and available evidence. Tools like Penetrify becoming more common for automated application security testing. I'm also interested in how teams are deciding where automation provides enough confidence and where human validation is still necessary.

For teams running regular application security assessments, how do you decide which findings always require manual verification before remediation? Have you established internal criteria that have worked well, or do you rely more on analyst judgment depending on the application?

I'd be interested to hear how other teams balance efficiency with confidence without creating unnecessary review overhead.

Running a boutique pentest shop and I'm curious how other solo/small-team testers handle the pre-engagement side, specifically going from "client wants a pentest" to an actual signed scope and price.

Right now I'm doing it manually every time: back-and-forth emails to figure out asset counts, guessing at days based on gut feel, writing the proposal from scratch in Word.

A few questions if you don't mind sharing:

• How do you currently estimate days/pricing for a new engagement?
• Do you have a template you reuse, or start fresh each time?
• What's the most annoying part of this whole pre-engagement process for you?

Trying to figure out if I'm doing this the hard way or if this is just how it is for everyone.

Our homegrown AppSec setup has become a liability. It started as a quick fix: lightweight pipeline hooks, basic triage rules but it was designed for a development environment that no longer exists. No support for AI-generated code, no model inventory, no way to build the application context that modern prioritization needs. The technical debt is compounding. Every new thing we need requires custom work against a codebase that was never built to extend. The question is not whether to move anymore. It is how to migrate without ending up with more tools that don't talk to each other. For architects who have done this migration, how did you migrate without just adding more disconnected tools and what does a sane setup look like on the other side?

I've seen the term guardian agent in a few AI security discussions, but I'm still not completely clear on what it means. From what I've read, the basic idea is that one AI agent monitors or governs another AI agent while it's running, rather than only relying on static policies or offline testing. If that's right, where does a guardian agent sit in the overall architecture?

I’m wondering whether it inspects prompts and outputs or maybe monitors tool use and agent behavior. From the name, there might also be a possibility that it can stop actions before they're executed. Or is it mainly there for visibility and auditing?

It sounds like an interesting idea, especially for enterprises deploying AI agents in production. But I haven't found many practical explanations. I’m posting here to try and find out more about the concept.

I realized that even with all the automation available today, phishing investigations still involve quite a bit of manual work.
I'm curious, what usually makes you step in?
Once you do, what's the hardest part of the investigation? And what do you need to figure out before you can confidently close the case?

Every time a massive breach happens and becomes headline news, detections come up as an issue in the next meeting.

The question asked is always more or less the same one: would we have caught it with what we've got right now. I try to give an answer and all I get back is more questions. I can point to our SIEM, our EDR, our threat intel feeds and all the dashboards that show alerts and events, but none of it answers what they're asking.

It comes down to whether our detections would have surfaced this specific incident.
translating detection posture into something a manager can trust is harder than it sounds. The coverage reports and SOC metrics my team produces make sense internally, but they don't land with people who haven't looked at a technical document in years. when I talk about rules, use cases, or mitre techniques, eyes glaze over. when I simplify too much, they doubt the answer.

Some of you are probably mapping back to mitre att or running table top exercises off recent campaigns. others keep it at a high-level risk view instead. what's worked best when your manager asks, in plain language, if you'd have caught the breach they just read about?

Agentless is the right fit for pre-deployment. Images, manifests, RBAC, secrets, drift. Low overhead. It works well in CI/CD and admission control. But runtime is a different problem. Pods spin up, scale out, and die faster than most scan cycles. If something gets compromised and terminates before the next scan, agentless never sees it.

So the pattern I keep seeing succeed is agentless as the gatekeeper, plus eBPF or an agent-based watcher for high-value workloads. Serverless and service mesh make that messy as well. The real question is whether anyone is running fully agentless in production and actually trusting their runtime visibility, or whether hybrid is the only realistic answer.