Daniel sent us this one about the cybersecurity tool stack — specifically, once you've got a stateful firewall, how much more do you actually need? He points out that Check Point basically invented the stateful firewall in the nineties, which made home networks safe enough that random people on the internet couldn't just connect to stuff in your house. But now there's this huge array of additional security claiming to be additive — deep packet inspection, EDR, and a whole explosion of AI tools that monitor for illicit behavior. And he's asking, honestly, for most users, is there actually anything wrong with just running a basic firewall?
This is exactly the right question to be asking right now, because we're at this moment where the vendor noise is deafening. Every security company has an AI story. Every dashboard has fifty red alerts. And the honest answer — which the industry doesn't love saying out loud — is that for a lot of people, a basic firewall is genuinely fine. But the "for whom" part is where all the interesting detail lives, and that's what's worth unpacking.
Worth unpacking without the sales pitch, ideally.
So let's start with what a stateful firewall actually does, because the term gets thrown around, but the mechanism is elegant and specific. A stateful firewall — and this really was Check Point's breakthrough in nineteen ninety-four with FireWall dash One — tracks connection state. It maintains a state table. So when your laptop sends a SYN packet out to a web server, the firewall notes that you initiated that connection. When the SYN-ACK comes back, it matches it to the existing session and allows it through. If a packet arrives that doesn't match any established session, it gets dropped.
Which is fundamentally different from the old stateless ACL approach where you were just inspecting individual packets against a fixed rule set with no memory of what came before.
A stateless ACL is like a bouncer who checks every person's ID regardless of whether they just stepped outside for a phone call. A stateful firewall remembers you stepped out and lets you back in without the whole rigmarole. It maintains context.
That context is what made home NAT routers workable. You didn't need to understand ports. You just plugged things in and the firewall handled the bookkeeping.
But here's the thing that most people don't realize — and this is the hinge that the entire discussion turns on — a stateful firewall allows all outbound traffic by default. Every connection your devices initiate, the firewall says yes to. That made perfect sense in nineteen ninety-four when the threat model was inbound. People were worried about port scanning, direct exploitation of exposed services, someone brute-forcing their way into your FTP server. The firewall solved that. You closed all inbound ports and the problem went away.
The threat model flipped, and it flipped hard.
Today's primary attack vectors are overwhelmingly outbound. Malware phoning home. Ransomware reaching out to command and control infrastructure. Credential theft beaconing. None of these are blocked by a stateful firewall because technically, from the firewall's perspective, your compromised laptop initiated the connection. The state table has a matching entry. Packet goes through.
The bouncer at the door doesn't check what you're carrying out of the club.
The stateful firewall is a bouncer checking IDs at the entrance. It has zero visibility into what's leaving the premises. And in twenty twenty-six, if you look at the numbers, this isn't theoretical. The twenty twenty-five Verizon DBIR — the Data Breach Investigations Report — found that sixty-seven percent of breaches involved a compromised endpoint credential. That's not an inbound exploit. That's someone with a valid key walking through the front door, then emptying the filing cabinets through the loading dock while the firewall waves them through.
The CISA report from twenty twenty-four — I think you mentioned this one — something like seventy-three percent of initial access breaches now use HTTP or HTTPS.
Seventy-three percent, yeah. Port four forty three. To a stateful firewall, that's indistinguishable from someone browsing Google News. The packets look identical. Same protocol, same port, same TLS handshake.
Which raises a question I think a lot of people have at this point. If the firewall can't tell the difference between legitimate web browsing and data exfiltration, how does it ever catch anything modern at all? Is the stateful firewall just...
Not obsolete — necessary but insufficient. Think of it like the lock on your front door. It stops someone from just walking in off the street. That's not a small thing. But it does nothing if the person already has a key, or if they're already inside. The stateful firewall eliminates an entire class of unsophisticated inbound attacks — and there are still plenty of those. Shodan scans, automated exploit attempts against exposed RDP ports, that kind of thing. It's just that the attacks that actually succeed against organizations with anything worth stealing have largely moved to different vectors. So the firewall remains the foundation, but you can't mistake the foundation for the whole building.
Which is where deep packet inspection comes in, or at least claims to.
Right, so this is the first layer that gets layered on top, and it's worth understanding what DPI actually does and doesn't do. Deep packet inspection goes beyond header inspection. A stateful firewall looks at the envelope — source IP, destination IP, source port, destination port, protocol, flags. DPI opens the envelope and reads the letter. It reassembles TCP streams, decodes the application-layer protocol — HTTP, DNS, SMB, whatever — and then applies signatures and heuristics to the actual content. So a stateful firewall sees a DNS query go out on port fifty-three and says "legitimate traffic." A DPI engine examines that DNS query and notices it's three hundred bytes long, contains base sixty-four encoded data, and is going to a domain that doesn't resolve — classic DNS tunneling.
You can run that either on the firewall itself or on a separate appliance — Suricata, Snort, things along those lines.
Suricata and Snort are the main open-source engines, and the rules work in two modes. Signature-based detection matches known patterns — if the traffic matches a known Cobalt Strike beacon packet structure, it fires an alert. Anomaly-based detection profiles normal behavior and flags deviations. The practical tradeoff is eternal and hasn't really changed since the nineties: strict signatures mean fewer false positives but miss novel attacks. Anomaly detection catches novel attacks but generates so many false positives that your SOC team wants to quit and become goat farmers.
It's the security equivalent of a smoke alarm that also goes off when you're making toast. Eventually, you take the batteries out.
That's not hypothetical. The Ponemon Institute study from June twenty twenty-five — this is very recent — found that SOC teams are dealing with an average of forty-seven hundred alerts per day. Twenty-eight percent of those require manual investigation. You do the math — that's over thirteen hundred alerts per day that a human has to look at. If your DPI is generating alerts that don't matter, you're making the problem worse, not better.
Can we put that number in human terms? Thirteen hundred alerts requiring manual investigation. If an analyst spends even five minutes on each one — and five minutes is absurdly optimistic for anything that's not an instant dismissal — that's over a hundred hours of work. You'd need a team of twelve analysts just to triage, never mind actually investigate or respond.
Most organizations have maybe two or three security people total. The math doesn't work. So what happens in practice is alert fatigue. Analysts start dismissing things quickly. They develop heuristics — "I've seen this alert pattern before, it's always a false positive" — and they stop looking. Which is exactly how the one true positive slips through. I've talked to SOC managers who say their biggest fear isn't a novel attack, it's that the attack is already visible in their logs and someone marked it as resolved without looking.
DPI can catch DNS tunneling, which a stateful firewall misses. But it's not exactly set-it-and-forget-it. There's a cost in tuning, in false positives, in the expertise needed to manage it.
There's another limitation, which became very visible in the MGM Resorts breach in twenty twenty-three. The attackers didn't send suspicious network traffic. They didn't use exotic protocols or weird ports. They used social engineering to gain valid VPN credentials, logged in legitimately, and moved laterally through the network using standard administrative tools. The stateful firewall saw an authenticated VPN session. DPI saw encrypted TLS traffic. Nothing in the network layer flagged this at all, because the behavior that would have detected it — a single set of credentials being used from an anomalous geolocation, then lateral movement via RDP to servers that user had never accessed before — that's not visible in network traffic. That's endpoint behavior.
That's where EDR enters the chat.
Endpoint detection and response. CrowdStrike Falcon, SentinelOne, Microsoft Defender for Endpoint. These became the must-have layer after the SolarWinds breach in twenty twenty-one and the MOVEit breaches in twenty twenty-three. EDR monitors things that no network appliance can see: process creation, registry changes, DLL loads, inter-process communication. When Cobalt Strike — probably the most widely abused adversary simulation framework — runs on an endpoint, it often uses process injection. It starts as a legitimate-looking beacon process, then injects itself into svchost.exe or another trusted Windows process. The TLS traffic to the C2 server, on port four forty-three, looks completely normal to your firewall and your DPI. It's encrypted. You can't inspect it without breaking the TLS — which is possible, but that's man-in-the-middle inspection and it introduces a whole different set of problems involving certificate stores and breaking certain applications.
The EDR sees the process injection.
Sees it instantly. Sees the beacon executable writing its memory space into svchost.That's not normal behavior and there's no legitimate reason for it. The network layer is blind but the endpoint is screaming. That's the complementarity — it's not that EDR replaces the firewall, it's that they see entirely different things.
Can we model this as layers on a cake where each one has a specific, defined blind spot that the next layer addresses? The stateful firewall is blind to traffic content. DPI sees content but is blind to encrypted payloads unless you break the TLS. EDR sees endpoint process behavior but doesn't see network flows. And so on.
That's a really good way to think about it, and it also maps to why you might not need all the layers. Each one addresses a specific failure mode of the layer below. But crucially, you accumulate false positives with each layer too. And you accumulate management overhead. There are real operational costs to adding these tools.
The cost of the widget isn't the money you pay for the widget. It's the people who have to stare at the alerts.
The people who have to tune it, update it, investigate the detections, write the runbooks. Most organizations buy tools they can't fully staff. Which brings us to what everybody's been selling nonstop since late twenty twenty-two — AI-powered security.
Here's where the marketing gets loud and the signal-to-noise ratio takes a dive. So what is AI actually adding?
Two different things. First, there's what we might call classical ML-based anomaly detection, which has been deployed in tools like Darktrace since roughly twenty thirteen. You train a model on what normal network behavior looks like for your environment, and it flags deviations. Johannes from accounting suddenly downloading forty gigabytes at three a.from a server in Moldova — flagged. This approach absolutely catches things that signature-based tools miss. The tradeoff is it also flags entire categories of normal but unusual activity, and you spend the first six months teaching the model what your network actually looks like.
Let me push on that six months figure, because I think it hides a lot of pain. What does "teaching the model" actually look like in practice?
It looks like an analyst receiving an alert at two in the morning because the finance team ran their quarterly close process, which involves pulling large data sets from an external auditor's SFTP server — something that happens four times a year and looks, to an untrained model, exactly like data exfiltration. The analyst investigates, determines it's legitimate, marks it as a false positive, and the model gradually learns that this specific pattern — this user, this server, this time of quarter, this data volume — is normal. Multiply that by every department, every quarterly process, every weird-but-legitimate workflow in your organization. For the first few months, you're essentially paying your security team to be machine learning trainers rather than incident responders.
If you don't have the staff to do that training, you either accept the false positives and burn out your people, or you widen the model's tolerance thresholds and risk missing actual anomalies.
The model is only as good as the feedback loop, and the feedback loop requires humans who understand both the technology and the business context. That's a scarce combination.
Then there's the generative AI piece, which is the more recent thing.
The tools like CrowdStrike's Charlotte AI, Palo Alto's XSIAM AI assistant, SentinelOne's Purple AI. These don't detect attacks directly. What they do is augment the SOC analyst's workflow. Instead of an analyst manually hunting through logs to trace a detection through the kill chain, you ask a natural language question — "what did this user do in the three hours after the suspicious email attachment was opened" — and the AI assistant correlates telemetry across tools and presents a timeline. It reduces triage time from forty minutes to potentially five. That matters when you're drowning in thirteen hundred manual alerts per day.
The honest framing isn't "AI detects zero-days and replaces your security team." It's "AI helps your analysts not drown.
It's important to say the limitation clearly: most AI security tools today are trained on supervised learning — known attack patterns. They're better at detecting slight variants of known techniques. But a novel attack chain, something that looks nothing like the training data, these models still struggle. There's no AI that sits in the corner and says "I just spotted a completely new class of exploitation technique that has never been seen before, never on any training set, and I understood it." The headlines suggest that. The reality is more pedestrian.
The AI is the musical equivalent of beige wallpaper — it covers the surface but doesn't invent new genres.
Which is not to say it's useless. Reducing triage time by eighty percent per alert translates directly to analyst bandwidth, which translates to fewer missed detections. But the realistic ceiling, in twenty twenty-six, is augmentation, not replacement.
There's a subtle danger here worth naming. If the AI assistant is doing correlation and summarization, and it gets something wrong — misses a connection, draws an incorrect inference — the analyst who's relying on that summary might never know. They're making decisions based on a digested view they didn't assemble themselves.
That's the automation bias problem, and it's real. Studies in aviation and medicine have shown that when an automated system provides a recommendation, humans tend to trust it even when their own judgment would have caught an error. In a security context, that means an AI-generated timeline that omits a critical event could lead an analyst to close an incident prematurely. The countermeasure — and this is something the better tools are starting to build in — is what they call "explainability." The AI doesn't just say "this is suspicious," it shows you which specific events triggered that assessment and lets you drill down into the raw logs. But that requires the analyst to actually drill down, which takes time, which somewhat defeats the purpose of the time-saving tool.
We're back to the same fundamental constraint: you need skilled humans in the loop, and those humans are expensive and scarce.
There's no tool that eliminates that constraint. There are tools that make each skilled human more effective, but the constraint itself is structural.
Alright, so we've built up three or four layers — stateful firewall, DPI or IPS, EDR, AI-assisted triage. At this point, the reasonable person listening is probably calculating whether they personally need any of this. So let's break it down by use case.
The answer really changes depending on who you are and what "weird stuff getting out" means for you. I'll propose a framework.
Category one: the home user. IoT devices, a laptop, a phone, maybe a NAS if you're fancy. The actual threat model here is your router getting compromised and conscripted into a botnet, or malware that arrives via a malicious ad redirect or a compromised software update. For this person, a stateful firewall plus basic DNS filtering covers almost the entire risk surface. The DNS filtering bit is important — services like Quad9, Cisco Umbrella, or even NextDNS will block known malicious domains at the resolution layer so your devices can never even connect to them. The stateful firewall keeps unsolicited inbound traffic out. The DNS filter stops outbound connections to known bad destinations. Together they handle upwards of eighty percent of realistic home threats.
Critically, none of this requires you to be a security professional who stares at a dashboard. It's set and forget.
Set and forget. The Google twenty twenty-five Threat Analysis Group report found that eighty-nine percent of malware families use DNS for command and control communication. That one extra piece — DNS filtering — knocks a huge percentage of threats off the table with almost no management overhead.
Eighty-nine percent. That's a number that deserves to be shouted from a few more rooftops.
I mean, if you block DNS resolution for known malicious domains, nine out of ten malware samples can't phone home, full stop. Whatever payload has been delivered, it's like giving it a phone with no cell service.
What about plugging your home printer into a VLAN so it can't talk to the rest of your network? Am I overthinking this?
For a home user, you're probably overthinking this. VLAN segmentation matters when a compromised IoT camera can traverse laterally into a server running financial data. But if your smart TV gets a ride on a botnet and the only thing on your VLAN besides the TV is your toaster, the blast radius... well, you might get some slightly browned toast.
Forensics would be a nightmare. "Sir, we've traced the breach to your kitchen appliance. The toaster was an unwitting accomplice.
The crumb-trail analysis alone would be a nightmare. But seriously, the home user's real defense is simplicity. Every additional control you add — VLANs, custom firewall rules, traffic monitoring — increases the chance you'll misconfigure something and create a bigger hole than you had before. The stateful firewall plus DNS filtering combination is powerful precisely because it has almost no configuration surface to get wrong.
Category two is the small business, say ten to fifty employees, handling customer personally identifiable information — maybe a law firm, a dental practice, a boutique marketing agency. The threat model upgrades significantly. Now you're looking at ransomware, business email compromise, and regulatory liability. Customer data leaking can literally shutter you.
The law firm doesn't have a SOC team.
No, and nobody's suggesting they build one.
The minimum viable stack here, in my view, is an NGFW — so your stateful firewall with DPI and application awareness built in — plus endpoint protection that has EDR capabilities, plus what's still the single highest-value control that almost everyone underinvests in, multifactor authentication on every external-facing login. That trio — NGFW, EDR, MFA — addresses the common remote access route, the standard malware delivery vector, and even a lot of insider threats. Add DNS filtering to that, which costs near-zero marginal dollars, and you've covered the overwhelming majority of the risk an attacker can bring against you without being explicitly targeted by a well-funded group.
If a well-funded group does specifically target you, there's not a lot any of these tools are going to do about it anyway.
Implicit threat model realism is the thing nobody puts on the slide deck.
Let's make that explicit, because I think it's one of the most honest things we can say in this conversation. If a nation-state actor or a sophisticated ransomware group decides your specific organization is the target, and they're willing to spend weeks or months on reconnaissance and custom tooling, your NGFW and your EDR and your MFA are speed bumps. They might slow things down, they might force the attacker to use a slightly more expensive technique, but they're not stopping a determined adversary.
That's not defeatism, it's just accurate scoping. The goal of the tools we're discussing isn't to stop a zero-day deployed by a state actor. It's to make you a harder target than the next organization, so the attacker moves on to someone with weaker defenses. Most cybercrime isn't targeted. It's opportunistic. You're not being hunted; you're being swept up in a net. And for that threat profile, the NGFW-plus-EDR-plus-MFA stack is effective.
Category three is where things get heavy and you start shopping for acronyms like CIEM and CWPP, which sound like mid-tier British railway companies and have just about as much user-friendly appeal.
Once you're managing regulatory compliance frameworks — HIPAA, PCI-DSS, SOC two — the playing field shifts from risk reduction to demonstrable controls. You are now buying tools for audit as much as for operational defense. I should mention that PCI-DSS version four point zero became effective in March twenty twenty-five, and it requires "automated security testing" and "continuous monitoring," phrases intentionally vague enough that auditors accept EDR logs and SIEM alerts as sufficient evidence. This is a huge part of the market dynamic. Organizations buy tools at least in part because they check compliance boxes, not because the threat model rigorously demands them.
Threat led versus compliance led is basically two entirely different security philosophies defending the same organization.
Right, and compliance-led security tends to produce fat stacks of tools that are deployed but not well-operated. The acronym wall goes like this: You add a SIEM — security information and event management — something like Splunk or Elastic to centralize log analysis. You add a SOAR — security orchestration, automation, and response — to build playbooks that automate incident workflows. If you're cloud-native, you might deploy a CWPP — cloud workload protection platform — which extends endpoint protection to containers and serverless functions. If SaaS applications are central to your operations, you'll add a CASB — cloud access security broker — that sits between your users and Office three-sixty-five or Salesforce and enforces policies on data access.
All of these are additive to the base firewall, not replacements for it.
Almost none of them talk directly to the firewall at all. They're monitoring entirely different planes. And this is where the picture shifts from "which tool do I buy" to "do I know what my actual attack surface looks like"? The twenty twenty-four Change Healthcare breach is instructive here. The attack used stolen credentials for a Citrix portal, and that specific Citrix portal had MFA disabled. No NGFW, no DPI engine, no SIEM would have stopped credential login followed by lateral move using the same legitimate access channel. The security failure was in the identity layer.
It falls into setup cost and admin overhead more than any tool deficit.
This is where Aria's framework — admittedly, I'm lifting from Daniel Shirelmann, the founder of Aria, from their documentation — but they talk about identity as the perimeter now. The breach went exactly where identities were misconfigured.
Another Israeli outfit — Aria security — which might bring us back, almost lazily, to the Jerusalem side of all of this.
Sure, I mean, it is well-documented that Israel has an unusual density of cybersecurity specialists and startups originating in cyber intelligence units. It's not magical, it's an output of a big pipeline investment spanning decades. Technically expert training, early operational exposure to real state-actor threat landscapes. It produces companies that tend to orient toward practical, high-signal style engineering. Check Point with FireWall One as the foundational ancestor, then over the decades Imperial BaRR, Cyverselyte — all the way today to Dominense plus an observable emerging hot area around generating cleanly abstracted security identity plumbing tools.
In deep, does that field advantage make their tool selection insights widely portable, or is someone in Birmingham looking appreciably at the exact checklist with threat types not mapping straightforwardly over?
Attacks reward asymmetry always, but common near-peer escalation scenarios trickle downwards noticeably. The geo-respecting guardrails and the modular deploy preview filtering are the bits which domestically under discussed, basically match high end caution layers already behind normally firewalled ordinary internal commerce gate paths, anyway we're wandering off topic — broadly usable recommendations sound extremely monotonous from Detroit to Istanbul parsing comparative head-value stack print.
Let that not get lost.
Run the threat scenario honestly instead of open table bulk consume catalog feature comparing confusion gets smaller right after exact acceptable loss total enumeration. Tools chart past low serious mileage stay decisive sharply mapping percentage coverage needle fractional realistic drift essentially shorter clear conclusion possibly misleading warm. Oh wait from here does Daniel is plug query cost in layered metric case, else step bring actual snapshot basically twenty percent effective defense budget gives eighty grip across, plain caution.
Anyway, I think the core's been hit — there absolutely is a large tool ocean beyond big base boxes and the minimum viable story depends narrower exact risk appetite answers decision chain from modest one-employee on cloud browser profiles through multisite enforced zero-rather than perimeter-saying apparatus that shrink obviously toward certain top-level compliance locked bar placements. DNS simple one worthwhile above near universal appliance use sitting humble catch steady additional win visible statistical leakage shrink down mass lateral crawling bounce absolutely far easier when zone trust identity aware ahead placed wherever free time cost trivial install permitting decent network closure completeness in pure measurement goal track decent certain.
If we're trying to land this plane for Daniel and for anyone else trying to navigate the tool stack question, what's the actionable takeaway? You've got a stateful firewall. You're wondering what to add. How do you think about that decision without getting lost in vendor feature matrices?
I'd say the decision framework has three questions, and you ask them in order. Question one: what is my actual threat model? Not the threat model the vendor slide deck assumes, but mine. Am I a home user worried about botnets? A small business worried about ransomware? An enterprise worried about regulatory fines and reputational damage? Be honest about this. The answer determines which layers are even relevant.
Question two: for each layer I'm considering, what specific failure pattern of my current stack does it address? If you can't articulate that in one sentence — "I'm adding EDR because my firewall and DPI can't see process injection" — then you're buying a tool because someone told you to, not because you understand the gap.
Question three: do I have the people to operate this tool? Not just deploy it — operate it. Investigate its alerts. If the answer is no, you're not buying security. You're buying a box that will blink red while the breach happens anyway. And in that case, you're better off spending the money on a managed service provider who can run the tools for you, or on hardening the controls you already have — turning on MFA everywhere, implementing DNS filtering, patching aggressively. Those unglamorous basics will do more for your security posture than an unmonitored SIEM ever will.
That's a refreshingly honest place to land. The tool stack expands to fill the budget available, but the security outcome doesn't necessarily follow.
The industry doesn't love that message because it doesn't sell products. But it's true. The stateful firewall was a genuine breakthrough in nineteen ninety-four, and for a lot of people, it's still doing most of the heavy lifting. Everything you add beyond it should be a deliberate answer to a specific question about what you're actually defending against and who's going to run it. If you can't answer both of those clearly, you probably don't need the tool yet.
Daniel, hopefully that gives you a framework. Stateful firewall plus DNS filtering covers the home user. Add EDR and MFA and you've got the small business covered. Beyond that, you're in enterprise territory where tool selection becomes as much about compliance and staffing as about threat detection. And at every layer, the question isn't just "does this tool catch something my current stack misses" but "do I have someone who will actually respond when it does.
If the answer to that last question is no, fix that first. The best detection in the world is worthless if nobody's looking at the screen.
On that cheerful note.
Until next time.
