Episode 35: The full-spectrum blind spot SASE leaves behind

Adam

Welcome to the Deep Dive, where we take the sources you provided, all the articles, research your notes, and deliver the insights you need to get up to speed fast. You came to us with a big one. It's probably one of the biggest topics in security architecture right now. Secure access service edge.

Christina

Or sassy, as everyone calls it. And it's everywhere, and honestly, it should be. I mean, the whole idea of converging your wide area, networking your SD-1 with key cloud security features, and delivering it all from the cloud, it just makes so much architectural sense.

Adam

Especially for anyone dealing with a distributed workforce, right?

Christina

Oh, absolutely. If your people are everywhere relying on cloud apps, this model is a lifesaver.

Adam

The promise is uh it's very elegant. You get this centralized uh policy engine that enforces security no matter where your user is. But as we were digging into the source material you sent, uh a pattern started to emerge. A really critical distinction that, you know, it gets a little blurred in the vendor slide decks.

Christina

It does. It's an easy mistake to make, actually, confusing a brilliant piece of architecture with a complete security capability. And that's really what we're here to nail down. SASE, at its core, is a connectivity and policy enforcement layer. That's its mission. But that mission, by its very nature, means it is not a detection and response capability. And confusing those two, well, it leaves this significant opening, what we're calling the SASE visibility gap.

Adam

And it's a gap that sophisticated attackers are, as we speak, actively exploiting.

Christina

They are.

Adam

So that's our mission for this deep dive. We're gonna unpack that distinction, give SASE its due for the problems it does solve, and then pinpoint exactly what's missing, and more importantly, how you can close that gap.

Christina

Right, and let's start by, you know, celebrating what SASE gets right. It has been a massive success in solving the old perimeter problem.

Adam

Oh, it's a huge success story. It's all about convergence. It pulls together networking functions like traffic optimization and SD-1 with these absolutely essential security controls. We're talking about things like firewall as a service, secure web gateway, and CASB.

Christina

And we should probably break a couple of those down just quickly. CASB, the Cloud Access Security Broker, that's crucial because it's what governs access inside Cloud Apps you don't even own. Think Salesforce or Microsoft 365. It gives you control over your data in their house. And then there's ZTNA, zero trust network access. That's what finally gets us past the uh the old slow, clunky VPN model. It makes sure access is granted based on who the user is and the health of their device, not just because they're on the network.

Adam

And the big architectural win here is it solves a real-world latency problem. In the old days, if a remote user in, say, London needed their traffic inspected, it had to get backhauled sent all the way back to a data center in New York. It's SASE just gets rid of that.

Christina

Yeah, it pushes the security and the policy right to the edge, right where the user is. It solves that connectivity and access problem brilliantly.

Adam

So platforms like Cato Networks or Netscope, they deliver genuine value for that specific problem.

Christina

They do. They solve tangible, really difficult problems. They secure that path from the user's device all the way to the cloud resource. They've earned that market share.

Adam

And that success is exactly why the term is everywhere. But this is the bridge to the uncomfortable part. If SASE is so good at securing the path, why isn't the final word on security?

Christina

It's because of that architectural difference we mentioned. SASE has to be optimized for low latency, real-time decisions. It's looking at a packet and has to decide instantly. unknown 3:44 Okay.

Christina

Block or allow based on policy. Okay. Detection and response, on the other hand, it needs something totally different. It needs historical data, behavioral modeling, complex correlation. You're looking for patterns across days, even weeks. Those functions, they're just better suited for, you know, cloud data lakes and specialized XDR platforms. SASE isn't built to be that.

Adam

Ah, okay. So SASE is the real-time traffic cop. It's enforcing the speed limits, making sure you have a license, but it's not the detective who shows up after the crash to piece together what really happened.

Christina

That's a perfect analogy. Exactly. SASE watches the door, but modern threats they don't knock anymore. They've found other ways in.

Adam

So let's get specific about this limitation. What is SASE not designed to do?

Christina

Okay. It does not hunt for threats that have already slipped past those initial controls. It doesn't have the specialized human analysts on the other end to investigate anomalies. It just logs them. And most critically, it does not correlate signals across your entire environment.

Adam

And that's the key. It misses the big picture, the narrative of an attack that crosses multiple systems.

Christina

Yes. And we know where the bad guys are focused now, right? They're not trying to brute force a firewall. They're coming in through compromised identities, size application abuse, and then they're just moving laterally once they get that first foothold.

Adam

The scary part is that SASE is actually seeing this activity, isn't it?

Christina

It is. That's the thing. Your SASE platform is logging all of it the access events, the policy blocks, the weird DNS queries. But unless something is actively stitching those logs together with, say, data from your identity provider or your endpoint tool, that information just sits in a dashboard.

Adam

So you technically have the data.

Christina

That is the sassy visibility gap in a nutshell.

Adam

Which means we've just traded a connectivity problem for a data overload problem.

Christina

Precisely. Just think about the sheer volume. It doesn't matter if it's Netscope, Cato, whatever, you are drowning in telemetry. Firewall logs, DNS queries, access alerts. The golden signal you need to find an attack is in there somewhere, but it's buried in tens of thousands of harmless events every single day.

Adam

But the question really changes, doesn't it? It's not do we have Sassy anymore? It's who or what is actually watching what Sassy is telling us.

Christina

And that's where the lack of correlation becomes so dangerous because individually these logs look like background noise. But when you correlate them, they reveal the entire attack narrative, the whole kill chain.

Adam

Okay, let's use the scenarios from the sources. This makes it really concrete. Scenario one, the suspicious access log. Your sassy solution logs a user, we'll call her Sarah, accessing a high-risk file sharing site at two in the morning.

Christina

Right, and on its own, that's well, it's just suspicious. Maybe Sarah's a rock star employee pulling in all nighter. Without more context, you dismiss it.

Adam

But the detective work starts when you connect that sassy log to other things. What if your identity provider, like Okta, saw three failed MFA attempts on Sarah's account an hour earlier from a totally different country?

Christina

Uh, see, the context changes everything instantly. It's not Sarah working late anymore. It's someone failed to get into Sarah's account, and then an hour later, a strange connection was made.

Adam

Okay, so now let's add a third piece. Data from your endpoint detection and response tool shows a brief unsigned PowerShell script ran on Sarah's laptop just minutes before that 2 a.m. connection.

Christina

And now you have the full story. Failed identity compromise, successful endpoint compromise, and then data exfiltration through that sassy connection. Three different systems identity, endpoint, and SASE. Each reported something that was, on its own, maybe a little weird, but together.

Adam

Together they scream active breach.

Christina

They scream it. That is the power of correlation. Now let's take it a step further with the full kill chain from scenario two. This is where multiple systems get hit one after another.

Adam

Okay, so starting at the entry point, email security flags a phishing link, but it's low risk, so it lets it through but logs the alert.

Christina

The user clicks, of course. Their endpoint tool then sees a brief fileless PowerShell execution. That's the attacker dropping their initial payload.

Adam

And at that point, the attacker's in. They need to phone home to their command and control server, and here's where SASE picks it up.

Christina

Right. The SASE platform logs an outbound connection to a weird, never before seen domain that's categorized as malware distribution.

Adam

So if you're looking at those three things in three different dashboards, it's just noise.

Christina

Complete noise. Email flags, thousands of links. Endpoints run PowerShell all the time.

Adam

But when a machine learning engine connects them, same user, same 10-minute window following the exact stages of a known attack, that's not just suspicious anymore. That's a confirmed kill chain in progress.

Christina

And that needs an immediate automated response, not a review in next week's dashboard meeting. The problem for MSPs and internal teams is they just don't have the tools or the people to make those connections in real time. SASE gives you the data, but something else has to connect the dots.

Adam

So that leads us to solutions. For anyone listening who's managing security, there are really three options on the table for closing this gap.

Christina

Right. Option one is the traditional way. Built it yourself, build an internal security operations center.

Adam

Which means hiring dedicated security analysts, training them to watch the SASE's dashboard along with everything else, and hoping they can correlate it all manually.

Christina

And that's well, it's incredibly hard. A good analyst costs well into six figures, and they still can't watch all those screens 24/7. It's just not a sustainable model for most businesses, even if you have the budget.

Adam

Okay, so if building it yourself is out, what about option two? This is the one I hear a lot. Just assume your existing MDR, your managed detection and response provider, has it covered.

Christina

And this is where we have to wave a big red flag. You have to check your contracts, you have to check the technical specs. Most established MDR solutions are endpoint focused. They grew up on the endpoint, which means they simply do not ingest SASE telemetry.

Adam

Wait, why not? If the data is so important, why wouldn't they just, you know, add a connector for it? Seems like a no-drainer.

Christina

It's a great question, and it goes right back to that architecture point. SASE data is fundamentally different. It's a massive astronomical volume of low-context network data. Integrating that properly behaviorally, not just dumping logs, takes a huge engineering effort. The sources were clear that providers like Huntress, for example, don't natively ingest or correlate telemetry from a SASE platform like Cato.

Adam

So you're left with two really powerful, really expensive security tools that don't talk to each other. You've just rebuilt the silo problem.

Christina

You've rebuilt the silo, which brings us to option three, and really the only practical way forward sassy XDR integration.

Adam

Using an open extended detection response or XDR platform.

Christina

Exactly. These platforms are designed from the ground up to ingest data from all these different sources endpoint, cloud, identity, and crucially SASE.

Adam

And that centralization is what lets a correlation engine instantly connect a SASE alert with, say, a weird login attempt from your identity tool.

Christina

It gives you that whole network visibility you need to see the entire attack from start to finish. And we have to be crystal clear here. SASE and XDR are not competing, they are complementary technologies.

Adam

That's the perfect way to put it. SASE handles secure connectivity and policy enforcement. That's its job. It does it very well.

Christina

An XDR or a really integrated SOC handles detection, correlation, and response. The mistake is thinking that just because you bought SASE, you've solved the whole security puzzle. You've only addressed the perimeter.

Adam

And this brings us to the business side of things. Knowing this completely changes how you should be talking about your security stack to clients or to your own board.

Christina

Yes. The translation layer becomes vital. Technical telemetry is worthless if the person signing the check doesn't understand the risk.

Adam

So instead of a report saying SAS detected anomalous egress traffic, you need to be able to say, we spotted unusual data leaving your network at 3 a.m. We correlated it with an identity takeover, and we shut the connection down in under three minutes. Here's what we think happened, here's what we did, and here's what you need to tell your board.

Christina

And that kind of clear, decisive statement is only possible if the correlation engine has already done the heavy lifting. It's the difference between reporting a potential problem and reporting a solved problem.

Adam

Which brings us back to how these integrations work. We mentioned some MDRs don't connect at all, but the source has also pointed out that even among XDRs, how they connect matters.

Christina

A lot, yes. For example, some tools might ingest sassy data as just a log feed, sort of like a traditional seam. And while that's better than nothing, it really limits your ability to do real-time behavioral correlation.

Adam

Because the logs don't have enough context.

Christina

Exactly. They lack the rich context and the granular timestamps you get from a native API connection. You're basically turning your fancy XDR into a simple log collector for that one source. It can't instantly connect a network event to an endpoint signal.

Adam

So the key isn't just if your platform ingests the data, but how? You need that native API integration for real-time correlation.

Christina

You absolutely do. So as you, the listener, are planning out your security stack, there are a few critical questions you just have to ask.

Adam

Okay, first, does your current security setup, your SOC, your MDR, whatever, actually ingest your SAS telemetry, or is that data sitting all by itself in a silo?

Christina

Second, can your tools truly correlate a SASE event with endpoint and identity signals in real time? Or are you just relying on slower log-based aggregation?

Adam

Third, who is actually responsible for watching for these cross-system attack patterns? Do you have the people or do you have the automation?

Christina

And finally, can you explain what SASE finds to your clients in plain business terms? SASE is a transformative tool, no question, but tools without oversight just create blind spots. And the real question is whether you're actively watching or just hoping that policy enforcement is enough.

Adam

And that brings us to the end of this deep dive. I think the core lesson is really clear. SASE is brilliant for solving the new connectivity perimeter, but you need XDR or a highly integrated SOC to address that visibility gap that sophisticated attacks create.

Christina

Never confuse policy enforcement with holistic protection. And maybe a final thought for you to explore on your own. Go research the real difference between SAMS style log aggregation and true real-time behavioral correlation in XDR platforms. Look specifically at how SASE vendors expose their telemetry. Is it just basic logs or is it a rich native API? Because how you get the data directly dictates the quality and the speed of the analysis. The ingestion defines the correlation.

Adam

Thanks for joining us for this deep dive. We hope you feel more informed and ready to ask some tough questions about your next security investment.