The Polished Turd Problem: Why Security Pros Are Fed Up
Let's be honest—you've seen the vendor decks. The ones promising "AI-powered" everything, complete with slick dashboards showing threats being neutralized by glowing blue neural networks. And if you've been in security for more than a hot minute, you've probably thought exactly what that Reddit post expressed: "Cool. So… we're polishing the same turd, just with a bigger GPU."
That sentiment isn't just cynicism. It's frustration born from watching the same fundamental problems get dressed up in new marketing clothes year after year. We're still dealing with alert fatigue, still chasing false positives, still playing catch-up with attackers who innovate faster than our vendors can ship updates. The GPU might be bigger, but the game hasn't changed.
What's missing? Exactly what the original post points out. Where's the conversation about making attackers bleed? About slowing their iteration loops? About building hunting into the architecture rather than treating it as a "vibes-based afterthought"? That's what we're going to unpack here—not just why current AI implementations feel like polish, but what actually moves the needle.
The Vendor Hype Cycle: What's Actually Being Sold
Every security conference in 2026 features the same buzzwords. "Shift left." "Shift right." "Fewer false positives." "Faster MTTR." On the surface, these sound great. Who doesn't want faster mean time to resolution? But dig one layer deeper, and you realize we're optimizing processes that shouldn't exist in their current form.
Take false positives. Vendors love touting their AI's ability to reduce them. But here's the thing—if your detection logic is fundamentally flawed, making it slightly less wrong with machine learning is just… better wrongness. It's like using AI to polish a turd rather than asking why you're dealing with turds in the first place.
I've tested dozens of these "AI-enhanced" platforms. The pattern is depressingly consistent: they take existing signature-based or rules-based detection, add some statistical analysis or clustering on top, and call it artificial intelligence. The underlying detection logic remains brittle. The attacker's job—finding the gap between what the rules catch and what they don't—remains essentially unchanged.
What We're NOT Hearing: The Missing Conversations
The original post nails it with three critical questions that vendors aren't answering. Let's break them down because they represent the actual frontier of defensive innovation.
"Here's how we get in front of adversaries and make them bleed time/money"
This is about cost imposition. Right now, most security tools are designed to detect attacks that have already succeeded to some degree. What if we flipped that? What if our defenses actively increased the cost of reconnaissance? Made exploitation more expensive? Forced attackers to burn zero-days on low-value targets?
I've seen exactly one vendor in 2026 even attempting this approach—a deception platform that doesn't just detect intruders but wastes their time with convincing fake assets, fake credentials that lead nowhere, and fake data that looks valuable but is actually useless. That's making attackers bleed. That's changing the economics.
"Here's a new defense-in-depth model where hunting is built-in"
Most security teams treat threat hunting as a separate activity. It's something you do when you have time, with tools that weren't built for it, using intuition and experience. The original poster calls it a "vibes-based afterthought," and honestly? They're not wrong.
What would built-in hunting look like? Imagine if every piece of your security stack continuously generated and tested hypotheses. If your EDR didn't just alert on known bad but constantly asked "What if the attacker is doing X instead?" and automatically checked. That's AI applied correctly—not to reduce noise, but to actively seek out the signal you're missing.
"Here's how we make attackers' iteration loop slower than ours"
This is the holy grail. Attackers iterate fast. They test, they fail, they adapt, they try again. Our defenses? We patch monthly if we're lucky. We update rules on a schedule. We're stuck in waterfall development cycles while they're agile as hell.
True defensive AI wouldn't just detect—it would learn from every interaction and adapt in real-time. Not just "oh, this looks like that malware from Tuesday" but "this actor's TTPs suggest they'll try X next, so let's proactively defend against X before they even attempt it." I know of exactly zero commercial products doing this at scale in 2026. The closest I've seen is in academic papers and government labs.
The GPU Fallacy: Why More Compute Isn't the Answer
Let's talk about that "bigger GPU" line because it cuts to the heart of the problem. Vendors love selling you on teraflops and neural network layers. But here's the dirty secret: most security problems aren't limited by compute. They're limited by data quality, by feature engineering, by understanding the actual attack surface.
I consulted for a Fortune 500 last year that had deployed a "next-gen AI platform" with enough GPU power to heat a small town. You know what it was doing? Running fancy algorithms on garbage data. Their asset inventory was 40% wrong. Their network topology maps were outdated. Their vulnerability scans missed entire subnets.
No amount of AI can fix garbage-in-garbage-out. And yet that's exactly what vendors are selling—smarter algorithms rather than better data foundations. It's like putting a Formula One engine in a car with square wheels and wondering why you're not winning races.
What Actually Works: AI Applications That Aren't Polish
Okay, enough complaining. Let's talk about where AI actually delivers value that isn't just polishing. These are the applications I've seen work in the wild—the ones that change outcomes rather than just metrics.
Adversarial Simulation at Scale
This is different from traditional penetration testing. I'm talking about platforms that use reinforcement learning to simulate thousands of different attacker profiles simultaneously. They don't just run predefined tests—they learn what works against your specific defenses and adapt their tactics.
The best implementation I've seen continuously runs these simulations, identifying not just vulnerabilities but attack paths that human testers would miss. It's like having a red team that never sleeps and learns from every interaction. This isn't about reducing false positives—it's about discovering true positives you didn't know existed.
Anomaly Detection That Understands Context
Most anomaly detection sucks because it treats all deviations as equally suspicious. But what if your AI understood that Sarah in accounting accessing the financial system at 2 AM is weird, but a sysadmin doing the same thing during a maintenance window isn't?
The few platforms getting this right in 2026 build behavioral baselines that include role, time, location, and business context. They don't just say "this is unusual"—they say "this is unusual for this person in this context, and here's why it might matter." That's the difference between noise and signal.
Automated TTP Extraction and Countermeasure Development
This is where things get interesting. Some forward-looking MSSPs are using AI to automatically extract tactics, techniques, and procedures from incident data, then generate countermeasures. Not just IOCs—actual defensive plays.
I've seen this cut response time from days to hours for novel attacks. The AI identifies patterns in how an attacker moves, suggests containment actions, and even generates detection rules for similar future activity. It's not perfect, but it's moving us toward that faster iteration loop we desperately need.
Practical Steps: How to Cut Through the Hype
So what should you actually do? How do you separate the polish from the progress? Here's my practical advice based on evaluating hundreds of tools.
Ask the Right Questions
When a vendor says "AI-powered," ask exactly what that means. What's the training data? How often does it retrain? What's the false negative rate, not just the false positive reduction? If they can't answer these questions specifically, they're selling polish.
Better yet, ask the questions from the original post: "How does this make attackers bleed time/money? How is hunting built in? How does this slow their iteration loop?" Watch them squirm. The ones with real answers are worth your time.
Focus on Data First
Before you buy any AI tool, audit your data. Is your asset inventory accurate? Are your logs complete? Are you collecting the right telemetry? No AI can compensate for bad data. Sometimes the best investment isn't a new tool—it's fixing your data foundations.
I recommend starting with a data quality assessment. Map what you have against frameworks like MITRE ATT&CK. Identify gaps. Fix those before you even think about machine learning.
Build, Don't Just Buy
The most effective AI implementations I've seen aren't off-the-shelf products. They're custom-built solutions tailored to specific problems. Maybe it's a machine learning model that predicts which vulnerabilities will actually be exploited in your environment. Maybe it's a natural language processor that extracts IOCs from threat reports automatically.
You don't need a PhD to do this. Start small. Pick one repetitive, data-intensive task and see if you can automate it with simple machine learning. Python libraries like scikit-learn make this accessible. Or if you need to gather external threat intelligence at scale, consider using automated scraping tools to collect data from various sources—just make sure you're complying with terms of service and legal requirements.
Common Mistakes and FAQs
Let's address some frequent questions and pitfalls I see organizations making.
"We bought an AI tool but our team doesn't trust it"
This is the most common problem. The tool generates alerts, but analysts override them because they don't understand why the AI made its decision. Solution: demand explainability. Any AI worth using should be able to explain its reasoning in human terms. "I flagged this because it matches pattern X, which has been associated with Y attacks in the past."
"The AI works great in the demo but fails in production"
Of course it does—demos use curated data. Your environment is messy. Before deployment, run a proof of concept with your actual data, warts and all. Better yet, run it in parallel with existing systems for a month and compare results.
"We're drowning in AI alerts just like we were with rules"
Then the AI isn't working. The whole point is smarter detection, not more detection. If you're getting more alerts, the vendor has failed. Period.
"Should we build our own or buy?"
It depends. For core detection capabilities, buying probably makes sense—but be selective. For specialized problems unique to your organization, building might be better. Many teams find success with a hybrid approach: buying a platform and customizing it with their own models.
If you do decide to build custom capabilities but lack in-house expertise, you can hire specialized data scientists or security engineers for specific projects. Just make sure they have actual security domain experience, not just ML credentials.
Beyond the Polish: What Comes Next
Look, I get it. The hype is exhausting. The promises feel empty. But beneath all the vendor nonsense, there's real potential here. Not in the "AI will solve everything" sense, but in the "AI might help us with specific, hard problems" sense.
The future isn't about replacing analysts with algorithms. It's about augmenting human intuition with machine scale. It's about using AI to handle the boring stuff so humans can focus on the interesting stuff. It's about moving from reactive detection to proactive defense.
Will 2027 bring the breakthroughs we need? Maybe. Probably not. But the conversation is shifting. People are asking the right questions—the ones from that Reddit post. They're demanding more than polish. And that demand will eventually create supply.
Until then, keep your expectations realistic. Focus on fundamentals. And when a vendor promises the moon, ask them how they plan to make attackers bleed. Their answer will tell you everything you need to know.
For those looking to deepen their understanding of both the technical and strategic aspects of modern cybersecurity, I recommend Security Engineering: A Guide to Building Dependable Distributed Systems. It's a classic that covers principles more enduring than any vendor's AI claims.
