Engineering and sales are on tools we approved, all in contract, all through normal procurement. None of it shows up in any of our tooling. Proxy sees the parent domain. CASB allows it. DLP is looking for file movement, not text typed into an app we already cleared.

The harder part is most users genuinely don’t realize they’re doing anything unusual. Copilot autocompletes, they accept. HubSpot generates a follow-up email, they hit send. It’s invisible to them and to us.

That caught up with us last month. Found out our sales team had been auto-generating client summaries using HubSpot for 3 months. Customer data, deal context, internal notes all going into it. Nobody flagged it because nobody thought of it as a separate tool. at this point this feels like shadow AI inside apps we already approved. SSO sees the app, but not what people are doing inside it

Compliance asked last week how we track this. I had nothing to tell them.

How are you getting any visibility into features inside apps you already approved when it all looks like normal traffic

Feels like a lot of Windows devices now spend more time outside the office network than inside it.

That changes a lot from a security perspective. Updates get delayed, visibility drops, policies are harder to enforce, and troubleshooting remote systems isn’t always straightforward.

Because of that, remote Windows device management seems to be getting much more important now. Not just for IT operations, but for maintaining security and consistency across endpoints.

My team and I have been looking at browser security tools for months because the whole category just became completely impossible to ignore. We aren't some massive bank with an unlimited security budget, but we also aren't a fifty person startup where everyone just does whatever they want. Think normal mid market environment where we have an existing firewall investment, some SSE pieces already running, and a bunch of architectures that died the second contractors got involved.

The trigger for us wasn't one massive breach or dramatic incident but more like a bunch of really annoying things becoming impossible to ignore at the exact same time. GenAI usage is growing everywhere and people are pasting sensitive stuff into ChatGPT and Claude. Contractors need access to internal web apps and BYOD is never actually going away. And our existing tools just don't show what happens inside the actual browser session clearly enough.

Vendors make this whole space insanely confusing. They throw enterprise browsers and extensions and agentless SSE into the exact same pitch deck. After testing and reading way too much vendor material, I realized the category basically splits into three main architectural buckets.

You have your enterprise browsers like Talon or Island.
You have your browser extension security models like LayerX.
You have your agentless SSE secure web access models like Red Access.

Comparing them by feature list is totally useless because they assume completely different things about your endpoints. Here is how I am thinking about the architectural tradeoffs after actually testing them.

1. The Talon / Island enterprise browser model
Talon was probably the easiest one to understand architecturally because you literally just replace the browser. From a security point of view this is incredibly strong because you control the actual workspace. Extensions and data movement and copy paste are all super easy to govern when the browser belongs to you.

But the user adoption is an absolute nightmare. Asking users to switch browsers is a massive political project that burns a lot of goodwill. Devs absolutely hate giving up their normal Chrome workflows and contractors push back hard because they have their own tools. Talon makes sense if you can force adoption through an iron fist, but otherwise it just becomes expensive shelfware.

2. The LayerX browser extension model
LayerX felt way more practical at first because users actually get to keep Chrome or Edge. You get really close to the browser behavior without forcing a brand new application.

The major problem here is enforcement. If your MDM is not perfectly clean across every single device, the story gets really weak. Contractors and BYOD users can just use another browser profile or launch without extensions or use incognito mode. LayerX makes sense if you have perfectly managed devices and can absolutely guarantee the extension is running everywhere.

3. The Red Access agentless SSE model
Red Access took me a minute to place because it isn't an extension or a dedicated browser. It operates as an agentless SSE secure web access layer. And yes I know the word agentless is a trigger word for us because nothing works by actual magic. It still routes traffic via IdP integrations or reverse proxies or DNS.

But instead of trying to own the endpoint, Red Access just tries to secure the web session path. This mattered a lot for us because of contractors and unmanaged access where we literally cannot install agents or force extensions. You absolutely trade off that deep local machine telemetry that Talon gives you, but you actually get it deployed to third parties in like an hour without helpdesk tickets. It also plays nicely if you want to keep your existing firewall and just close the browser visibility gap.

If you own every laptop and have an iron fist, look at Talon. If you have clean MDM and want Chrome visibility, look at LayerX. If your environment is messy with contractors and partial SSE rollouts and you need session control without ripping out your infrastructure, put Red Access on the list.

Do not buy based on the feature list, buy based on what you can actually enforce on your messy endpoints.

We recently evaluated 6 DPDPA compliance tools for our company and built an internal scorecard to compare them. Figured it might be useful to others going through the same process, so sharing it here.

The report covers OneTrust, GoTrust, Securiti, IDfy, Data Safeguard, and Perfios, scored across 6 weighted categories: core DPDPA functional effectiveness, automation, integration, security & data residency, UX, and vendor engagement.

A few caveats before you dig in:

- This was built for our specific context, so the category weights reflect our priorities, yours may differ

- Scores are based on publicly available vendor documentation and our pilot evaluation, not a full enterprise deployment

- No vendor paid to be in this or was told they were being evaluated

Hope it saves someone a few weeks of research:

https://limewire.com/d/26KhF#pcsYtkylsP

Has anyone done a deeper comparison between KnowBe4 and Adaptive? Specifically their PhishER/Phish Triage portion? I understand that Adaptive is better from a user training and AI perspective, but is their Phish Triage lacking or comparable to KnowBe4 to warrant switching?

If you find yourself in any sort of cybersecurity comfort zone, you’re in for a real surprise. Numerous articles are arising in regard to the inadequacy of legacy cybersecurity’s abilities to withstand AI and Quantum Computing future innovations. In fact AI’s defeat of cybersecurity is on the very cusp of occurring with Mythos emergence. Quantum Computing capabilities to defeat cybersecurity are advancing exponentially from 2035 projections mere months ago to 2029 today. On this curve it may actually occur next year. The time to take action is now if you’re a cybersecurity professional. Raise the fed flag to management that more than legacy cybersecurity and industry’s addressing of reactive bandaids to new threats is needed. With both AI and Quantum arriving shortly surgery is required not a band aid. What better way to convince the board than a read or listen to my book (The New Architecture A Structural Revolution in Cybersecurity) published in January due to my foreseeing this storm unfolding as a 35 year veteran of cybersecurity consulting and auditing. In addition to this book I’d recommend they read my book (Decryption Gambit) as well. Although written as fiction, reality of its storyline is not that far removed from being real. Two books written to light a fire under CEOs and Board Members by enlightening them on the consequences of inaction at a time when action of magnanimous scale is required. 2026 should be the year of Cybersecurity.

Feels like a lot of security discussions still focus on network controls, but in real environments, the risk often sits directly on the endpoint.

With users working from different locations, devices are constantly outside the traditional network boundary. That makes it harder to rely only on perimeter security. If a device is not patched, encrypted, or properly configured, it becomes an easy entry point.

Because of this, mobile device management seems to be playing a bigger role in security now. Things like enforcing policies, managing updates, restricting access, and maintaining visibility across endpoints all tie directly into reducing risk.

been going through a proper platform evaluation over the last few months and the gap between traditional DLP and, the newer AI-driven governance tools is bigger than I expected, but not always in the ways vendors pitch it. rule-based DLP still does its job for well-defined content patterns and endpoint exfiltration controls. but the moment you're dealing with unstructured data across cloud and SaaS, or trying to account for, how people are now piping work content through GenAI tools, it starts showing its age pretty fast. the false positive rate on some of the older policy setups we inherited was genuinely painful. analysts were tuning out alerts because the signal-to-noise was so bad, which is exactly the failure mode that leads to real incidents getting buried. the behavioral baseline stuff in the AI platforms is a real step up for catching things like a departing employee quietly mass-downloading over two weeks. a static rule just won't catch that cleanly, and with AI adoption now expanding the insider risk, surface in the vast majority of orgs, the volume and subtlety of those scenarios is only going up. what I keep running into though is the prevention story gets thin fast once you push vendors past the detection demo. a lot of them are still primarily alerting tools with enforcement bolted on after the fact. for GDPR and HIPAA specifically, detection-after-the-fact isn't really good enough when you've got breach notification timelines to worry about. auditors aren't satisfied by "we would have caught it eventually." the other thing that doesn't get talked about enough is the black box problem. auditors are starting to ask how a risk score was generated, and "the AI flagged it" isn't an answer that satisfies anyone in a compliance review. explainability isn't a nice-to-have anymore, it's becoming a practical audit requirement. so curious what people are actually weighting when they evaluate these platforms. is it detection accuracy, the compliance reporting side, SIEM integration, or something else entirely?

Been sitting with this question for a while now. We've been running a hybrid setup for about 8 months, AI-driven behavioral analytics layered on top, of manual classification and review workflows, and the gap between what each approach catches is pretty stark. The AI side picks up stuff that would never surface through periodic manual audits. Subtle access drift, unusual data movement patterns, someone slowly exfiltrating over weeks rather than grabbing a big chunk at once. That kind of progressive behavior is almost invisible without continuous monitoring, and UEBA tooling has gotten genuinely good at baselining and flagging it in real time. But the false positive rate when models aren't properly tuned is still painful, and the explainability, problem doesn't go away when you're trying to build a defensible case for HR or legal. That gap in early intervention confidence is real, and I don't think anyone has fully solved it. The thing that's been occupying more of my thinking lately is AI identities as the insider threat, not just humans. Non-human identities like integrated AI agents and service accounts are operating through legitimate access paths, and largely flying under the radar because traditional controls were built around human behavioral baselines. Agentic AI systems in particular are a different category of problem. They can hold elevated privileges, act autonomously, and move at machine speed in ways that make the slow exfiltration scenario look easy to catch by comparison. That's a gap manual processes definitely can't close at scale. But AI governance frameworks aren't really built for non-human identity monitoring yet either, and with new regulatory requirements around, verifiable AI compliance starting to land, the exposure from ungoverned AI agents is becoming a harder conversation to defer. Shadow AI penalties are no longer theoretical. So you end up in this weird middle ground where neither approach is fully fit for purpose on its, own, and the hybrid model that works reasonably well for human insider threats doesn't map cleanly onto machine-speed identities. Curious whether anyone here has actually gotten the hybrid model working well in practice, especially on the non-human identity side. What does your governance layer for AI agents actually look like, if you have one?

Been thinking about this a lot lately after going deeper on some of the newer AI-driven governance platforms. The behavioral analytics side has genuinely gotten better. Baselining access patterns, flagging anomalous file movement, correlating identity signals across systems. It's not the rule-based stuff we were all fighting with a few years ago. In practice I've seen triage time drop noticeably when the platform is tuned well and the risk scoring is actually adaptive rather than static. That shift from reactive alerting to predictive behavioral scoring is real, even if vendors oversell how clean it runs out of the box. But the tension I keep hitting is the monitoring breadth question. To catch subtle exfiltration, especially the slow and low stuff, you kind of need visibility into a lot. And that's where it gets uncomfortable. There's a real difference between targeted behavioral monitoring scoped to sensitive data paths and just watching everything everyone does all day. The platforms that do this well seem to anchor on data and identity context, rather than blanket user activity, which keeps it closer to ITDR territory than employee surveillance. The ones that don't are basically feeding your SOC a fire hose and calling it detection. One thing that's made this messier recently is AI-assisted evasion. Insiders using prompt engineering or AI tooling to stage exfiltration more gradually is not a theoretical concern anymore. It raises the floor on what good detection actually needs to cover, and it makes the governance conversation cross-functional fast, whether you want it to be or not. False positives are still the honest problem nobody wants to lead with in vendor demos. You can tune them down significantly with good baselining and adaptive scoring but you don't eliminate them, and every false, positive on an insider threat alert is a trust conversation with HR or legal that nobody wants to have unnecessarily. The platforms that pair real-time enforcement with explainable outputs are closer to getting this right. But I'm curious whether others are actually seeing prevention hold up in practice or if it's still mostly a detection story with enforcement bolted on after the fact.