Our network stack is fine, our proxy logs are fine but they don't always give us much context on what someone actually did inside an AI-native browser session. I am trying to figure out what people are doing for session controls, clipboard rules, download limits, prompt/data controls and the built-in AI features that can interact with page content. Were mid size, so I need something practical and not a giant deployment science project :/

Working through a vendor evaluation and going back and forth on this: agent based means another thing to deploy and maintain across every endpoint when the team is already stretched thin, while agentless sounds appealing on paper but I want to know the real trade off before I recommend one approach over the other.

Specifically trying to understand how each model actually delivers policy-based security day to day, not just at deployment. If you've run a POV on either side, what did you actually give up going agentless, and was the agent based overhead as bad day to day as it sounds up front?

Manufacturing, sites in the US, Europe and two in Asia and Singapore to Frankfurt has been the bane of my life for two years. We are shortlisting SASE now and every vendor keeps hammering their private global backbone like it is the whole reason to sign. I cannot tell yet if that backbone fixes my Asia problem. Might just be an expensive line on the quote.

Right now our M365 and a couple of SaaS apps get hauled to a central breakout and the Singapore office pays for it every afternoon. One vendor swears their backbone makes that disappear. The vendor who does not have a backbone swears their peering is so good I would never feel the difference, which is a convenient line coming from the one without a backbone.

I have a folder full of benchmarks and every single one flatters whoever made it, so I have given up trusting them.

What I need is someone running real global sites who can tell me the backbone moved the needle on their worst cross-region path, or that it did nothing a nearby PoP had not already handled.

We’ve been adding more AI workloads on prem and I’m realizing our current firewall layout wasn’t really designed with this kind of traffic in mind.

A lot more communication is happening between internal services now and some of those flows are pretty heavy. I’m trying to work out how much of that traffic people are realistically inspecting without creating a performance problem.

If you're doing this at scale, how are you separating AI infrastructure from the rest of the data center? Are you pushing more of it through dedicated firewalls or handling most segmentation closer to the workloads?

I’ve been working on De-code, an open-source CLI agent for coding and authorized security-testing workflows.

The goal is to build something that lives directly in the terminal and can combine agentic reasoning with coding, system interaction, and security tooling.

It’s still an early project, but the basic CLI is working and I’m currently focusing on the core agent/runtime, tool execution, and making the architecture extensible.

GitHub: https://github.com/AbhijeetKumar1505/decode

I’m sharing it early because I’d rather build this in the open and get feedback from people who actually work with these tools.

Contributions, architectural feedback, bug reports, and ideas are all welcome.

Most of our tabletop scenarios assume the breach originates inside our own environment, but our biggest actual exposure is probably a critical SaaS vendor or MSP getting popped and that blast radius hitting us through API access or shared credentials. It's hard to build a realistic exercise around a scenario where the initial compromise, containment options, and comms are all partially outside your control. Has anyone actually run a supply-chain/vendor-breach tabletop that felt real instead of hand-wavy? What did the injects look like?

With lots of ShadowIT and shadowAI, many organizations are worried about data controls. I was reading on the new products, safeguards, guardrails and what not. There are products out there who all are a variation of a Secure Web Gateway (or part of a SSE solution). They see the data in motion at their cloud tenant/deployed tenant and apply controls.

The issue as I see is there are two ways of reading traffic.

1. Explicit Proxy - Apply a proxy auto config file or configure the proxy plugin URL in to OS/Browser and tunnel the traffic in to the product SaaS tenant.
2. Install a Trusted root certificate - Breaks whatever certificate pinning, but essentially the rest of traffic is visble to understand and monitor.

Now the problem is most orgs have their firewalls or SSE products in place. If the existing product does not provide granular control over AI, they need a separate product to do it. Mostly due to budgets and ease of use.

If then the traffic needs to be visible to both products. How is this achieved? A process of serial processing at the endpoint?

The way I can think of is, do a local breakout to AI related traffic and the rest is taken through the main product Firewall/SSE.

Then it is again a manual process of figuring out what AI traffic is.

Trying to understand what others experiences are in this domain.

Asked the same question in r/cybersecurity

Edit - spelling

Figuring out where the line is with personal devices. ChatGPT is approved for work, we have rules around what people can put into it but then someone opens the same account from their own laptop and now i'm being asked how much corporate control we really want on a machine we don't own. I don't really want to enroll somebody's personal laptop into Intune or cover it in company software just to protect what they are doing with work data. Is there a decent middle ground here? same AI policy on BYOD without turning the personal device into a managed endpoint?

Security wants to slow AI tool adoption, engineering wants to move fast, and I'm in the middle trying to find something that doesn't tank velocity or just push everyone toward personal accounts and copy pasting code around, which is arguably worse than the risk we were trying to avoid in the first place.

Looking for what's actually worked for people, not just war stories, what did the real policy conversation with security look like for other eng leaders who've gone through this, and has anyone found an Endpoint Control and Prevention approach that doesn't slow devs down?

Hi all, CISO at a mid size org here. We have a small SOC, like 24x5 plus on call, and our SIEM bill and alert volume are both getting silly. Been looking at a few AI SOC tools and agent style triage systems that claim they can sit on top of the SIEM and handle enrichment, correlations and basic response.

For context we are already on a big name SIEM and an ok EDR, but our tier 1s are drowning in medium alerts and "suspicious but probably nothing" stuff. I keep getting pitched on AI detection engineering and AI based triage that can auto group incidents, score them, pull context from prod, and either close junk or hand a good story to humans. In theory that sounds great, but I am lowkey nervous about turning any of this loose on production data without some guardrails and good kpi visibility.

If you have tried any of the newer AI SOC platforms or agentic triage layers on top of your stack, would love to hear how you scoped the first use cases and what you let it touch at the start, appreciate any thoughts

I see lots of articles and discussions on AI-enabled traditional cybersecurity pentesting, but what a people doing or using for AI-security pentesting?

Hello Everyone!

So i am a cybersecurity intern leaning towards to GRC from a growing company. My background is Software Engineering and with that i dont really learn anything about Cyber Security during my studies. Maybe a bit of Netwroking but thats it. Im trying out Cyber Security to gain experience and get out of my comfort zone and for now i have already completed 1 compliance task with vendor and 1 Technical finding from our system and report to update the said software.

So back to the title, my task for now is to explore Nextcloud Spring and do a report of my findings and list out Why our company have to upgrade from 33 to 34. I understand Nextcloud Summer 26 is coming soon but for now I want to stick to my task. From researching about Nextcloud Spring 26 I came across Nextcloud Governance which is a new feature/app (im not sure which term to put).

I need a lil bit of help from the community to help me understand more about what this feature do. I understand this is a one overview compliance setup and offer tools for controlling data, track data access and track data sharing within the organization but I guess you could say that I need better understanding on how these tools works and how it will benefit us. Thank you so much.

I'm interested in the backend mechanics and data architecture of modern Trust & Safety engineering, specifically regarding ban evasion, identity tracking, and data retention queues.
Take a modern LLM or consumer web platform (like ChatGPT or similar organisations) as an example. Imagine a scenario where a user heavily violates the platform's terms of service (e.g. cyber abuse, severe violations like illegal activity and similar, or spam). Before the automated guardrails or manual review systems can finalize and push a formal "ban" status to the account, the user proactively triggers a standard "Delete Account" request to clear their record and bypass future identity tracking.
From a security and compliance engineering perspective:

How do modern authentication backends handle this race condition? Does a deletion request place the account data into a pending/quarantine state (like a 30 day queue) where automated safety pipelines still run to completion, or does the deletion process typically override active moderation scans?

When an account is in a standard deletion queue, how is the data architecture structured to comply with "Right to be Forgotten" mandates while legally preserving specific unique identity markers (hashed phone numbers, hardware tokens, device fingerprints) for persistent abuse blocklisting?

Once a severe violation is flagged on an account already queued for deletion, how does the system pivot that metadata out of the standard user database and into an immutable security blocklist to ensure they cannot immediately register with the same identifiers?
I’d love to hear insight from anyone working in application security, infrastructure, or Trust and Safety engineering on how these parallel pipelines talk to each other under the hood.

We keep adding dashboards and I'm not convinced we're actually more secure, just better informed about problems after they happen. Ai runtime visibility into what an agent is calling, what data it's touching, felt like the obvious first step, and it was, but visibility alone doesn't stop anything.

The team treats the visibility layer like it's the security control, when really it's just the input to a decision someone or something else has to make fast enough to matter. How do you think about the line between having enough ai runtime visibility to actually act on it versus collecting data nobody has time to review Is there a point where more visibility just becomes more noise?

A lot of infrastructure still relies on protocols and network architecture designed long before today’s threats.

AI doesnt need to invent new attacks . If it makes reconnaissance, fuzzing, discovery, protocol analysis and exploit development much faster, it will become much better at chaining multiple attacks together. A protocol flaw, exposed service, configuration mistake and compromised identity can be managed individually. The bigger concern is AI identifying how those weaknesses connect and building attack paths humans would take far longer to find.

1. Do we underestimate how much AI will increase offence and how does this make defence much harder?
2. Should we be redesigning the underlying communications stack rather than continually adding more security around existing protocols?

So... yeah. I work on detection engineering and continuous exposure validation, and we use this platform to simulate real world attack techniques in a lab tenant before we push anything toward prod.

For context, I was building a new scenario to validate some EDR and SIEM detections around lateral movement and data exfil. The idea was simple, hit the lab environment, validate coverage, get some nice dashboards for our CISO, all that. Except I fat fingered the target scope and selected our shared staging subscription that feeds into the same SIEM as prod. Then I kicked off a pretty noisy adversarial exposure run and went to grab coffee.

By the time I came back, our on call analyst had already escalated to the soc manager because the SIEM lit up like a Christmas tree with “real” exfil alerts from what looked like a legit service account. IR bridge spun up, execs pinged, people talking about containment while I was still trying to figure out why my lab run was so quiet.

I finally realized the agent group name in the platform did not match what I thought, checked the run, and saw the horrifying words “staging east cluster” in the asset list. I had basically launched a full attack simulation against the staging environment that everyone treats as almost prod, without any heads up or change ticket, and then walked away. The only reason this is recoverable is that it really was still contained to staging and no real data left, but it burned half a day of the soc and I feel so embarrassed.

Now we are doing a postmortem on “unauthorized simulated attack activity” which is technically me trying to do my job. I feel sick about this and lowkey terrified of the next detection validation run rn... anyone else managed to nuke trust like this with a lab gone wrong?

Our initial policy was effectively: "Do not paste sensitive information into public AI tools." The policy is sound, but it assumes people can make a correct classification decision while troubleshooting an incident, cleaning up a spreadsheet, writing code, or summarizing a customer conversation.

The real decision point is often the moment someone is about to paste text or upload a file. By then, a policy document is unlikely to change much.

Blanket blocking creates a separate issue because legitimate teams increasingly depend on approved AI workflows. We are looking at controls that add friction only where the content or action is higher risk—for example, protected data types, source code, customer records, or file uploads—rather than unrestricted access or a universal deny rule.

For teams that have deployed controls, what has actually reduced risky pasting or uploads without making approved AI use unworkable?

Our SAST tool flags a huge volume of findings every scan and the true-positive rate feels low once someone actually digs in. We've tried tuning rulesets and suppressing known patterns, but that just shifts the problem, either we suppress something real or we're still stuck manually confirming the rest...

I've seen a few vendors claim their AI can triage SAST output reliably, but I'm skeptical since most "AI triage" I've tested so far just re-ranks the same noisy list rather than actually determining exploitability. Has anyone found a rigorous way to benchmark this claim, or a workflow that's held up under real audit, not just vendor demo conditions?

Hi!
I compared org charts on theorg.com for a couple of companies I've worked at, and the structural data (reporting lines, titles) plus the "visual style" of employee avatars matched exactly what I'd expect from Microsoft Teams/Entra ID — including the default colored-circle-with-initials avatar Teams generates when someone hasn't set a profile photo.

This makes me think the data isn't just manually crowdsourced (as the platform claims) but is being pulled somehow from Microsoft 365/Graph API — either via an employee connecting their account, a browser extension, or some other sync mechanism.

So, here are my questions:

• Is there a known way third-party SaaS tools pull org structure + photos from Entra ID/Graph API without full admin/IT involvement (e.g. via a single user's OAuth consent)?
• Has anyone seen this kind of "shadow sync" happen at their org — where a well-meaning employee grants an app permissions that end up exposing broader directory data?
• What's the actual blast radius of a user granting User.Read.All or similar Graph scopes to a random SaaS app?
• Any recommended way to check Entra ID's enterprise app consent grants for this kind of thing?

Curious if this is a known pattern or something specific to how this platform operates.

I’m testing a read-only OSS PowerShell tool, that combines signals like owners, Azure RBAC, tags, relationships and recent activity to identify likely ownership.

No automatic assignment — just evidence for a human decision.

What signals do you actually trust when validating application ownership?

Cybersecurity Awareness Month is coming up and Im already seeing more campaigns lean into team metaphors again

Saw this post from Caitlin Sarian / Cybersecurity Girl with Hoxhunt using a World Cup style angle for awareness month

I get the idea behind it. The Hoxhunt angle of treating awareness more like ongoing practice and coaching instead of a once a year training module makes sense. People need repetition and some sense that security is part of everyones job, not only the security team’s problem

But Im lowkey curious how this lands with people here

Does the team sport analogy help non security employees understand the point or does it feel too cute and watered down for something tied to real incidents and business risk?

Also interested in what people are doing for October that doesn’t feel like the same recycled posters, quizzes and fake phishing emails

Liveness checks are good at detecting things like a photo or replayed video being presented to the camera. But there's a different problem when the attacker bypasses the camera and feeds manipulated video directly into the application.

How are security and identity teams handling this in practice? When evaluating verification systems, dos injection detection get tested alongside liveness/Presentation Attack Detection (PAD), or does the assessment usually stop at liveness?

Would be useful to hear how different teams approach this.

We're reviewing data security platforms and Varonis is obviously on the list, but I don’t want to default to it just because it’s the name everyone knows.

Our environment is pretty SaaS-heavy, with a lot of unstructured docs and shared data rather than neatly structured database fields. Classification accuracy is a big priority for us. I also don’t want to bring in another tool that has the security team spending half their time sorting through false positives.

For anyone who’s evaluated Varonis competitors recently, what else made your shortlist? And once you actually got into the POC, what separated the stronger options from the rest?

Hi! Bit of a basic question, but I’m helping out our organization meet some security requirements, and one of them is that our provider wants to disable POP3/IMAP for all the users in our organization because it breaks MFA. However, I have at least one dedicated Apple Mail application user, and I’m not clear on whether IMAP is required for that to function - it seems like maybe? Or if I have the user sign out and sign back in using modern OAuth there is some way where they can continue using Apple Mail and we can disable those protocols. Any advice (and explanation) appreciated!

just concluded a 60 day trial POV on the same M365 tenant all three on top of Defender for a 500 seat finance shop. we're doing this only because of vendor email compromise and clean BEC slipping through our current gateway. there's alot at stake so we need to get this right.

results so far are as follows. Abnormal caught the most payloadless BEC and had the lowest false positive rate of the three and Checkpoint was close, is cheaper for our size and can run inline with real attachment sandboxing while still protecting internal mail, which Abnormal does over API only. Sublime was very good at detection and far more configurable, only problem is it wants an engineer to run it. none of them is a clean win. For anyone who picked either of these in the last year, which would you recommend?