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Sometimes—but documented cases of deceptive cybersecurity marketing do not show that the industry as a whole is fraudulent. Federal Trade Commission (FTC) actions describe particular sellers that used false scan results or scare tactics, a privacy case involving browsing data, and allegations that a screening system’s detection capabilities were overstated. The practical lesson is to ask what a product actually promises, what evidence supports that promise, and whether its data practices match its privacy claims.
What does “snake oil” mean in cybersecurity?
Here, it means a product or service sold with deceptive or unsupported claims about its security benefits. That is different from ordinary sales language that sounds more confident than the evidence, and different again from a legitimate product that works well only when it is configured and used in the right environment.
The FTC’s cases provide evidence of specific conduct, not a measurement of how often it happens across the cybersecurity market. They are reasons to scrutinize claims—not grounds to assume every vendor, antivirus product, or AI security tool is a scam.
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1Clear out junk files and repair common Windows errors2Scan for outdated or missing drivers - takes under a minute3Repair Windows errors before they cause bigger problemsWhat documented cases show—and what they do not
False scans and scare tactics
The FTC described a 2008 scheme in which purported computer scans falsely reported viruses, spyware, or other problems and pressured consumers to buy software. Its consumer guidance has also covered fake scan results and repair products or technician services sold after consumers were told their computers were threatened. These cases show why an alarming result on a screen is not proof by itself; they do not establish how common this conduct is among legitimate security products. See the FTC’s 2008 case announcement, consumer guidance on tech-support scams, and guidance on spotting and avoiding tech-support scams.
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Privacy promises that conflict with data practices
In the Avast matter, the FTC said the company and subsidiaries sold web-browsing data after promising that their products would protect consumers from online tracking. The FTC case page says the order required a $16.5 million payment and prohibited selling or licensing browsing data for advertising. The order resolved FTC charges; it should not be read as a finding that every Avast product, or antivirus software generally, is a scam. The FTC page also records a December 2, 2025 update about consumer payments. Read the FTC’s Avast case page.
Allegations about AI screening performance
In November 2024, the FTC announced action concerning Evolv Technologies, alleging that the company overstated what its AI-powered screening system could detect and made misleading comparative claims. The announcement describes allegations and a proposed-settlement context; it is not evidence that AI-based security products as a class are ineffective. It is a useful example of why broad claims need a defined scope, test conditions, and a meaningful comparison. See the FTC announcement.
How to tell whether a security claim is meaningful
A claim is only useful when you can tell what outcome it promises and under what conditions. “Detects all weapons” or an unqualified promise to protect against threats is not self-validating. Ask for enough detail to understand what the product covers, what it excludes, and how its performance was assessed.
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Scan for outdated or missing drivers - takes under a minuteDriver Scan →Repair Windows errors before they cause bigger problemsFix Now →Fix the driver behind crashes, sound loss and screen glitchesFind Drivers →- Define the outcome: Does the vendor promise detection, prevention, blocking, recovery, or reduced risk? Those are not interchangeable.
- Set the scope: Which threats, devices, users, configurations, and operating conditions are covered? What falls outside the claim?
- Inspect the evidence: Ask which test, dataset, or operational results support the claim, who conducted the test, when it was run, and whether you can review the method.
- Check the comparison: If a vendor says its product is better, ask what it was compared against and whether the baseline and conditions are relevant to your environment.
- Ask about errors: Request information on false positives, false negatives, known blind spots, and how failures are handled.
- Verify the privacy story: Ask what data the product collects, where it is sent, how long it is retained, and whether those practices match the vendor’s privacy promises.
This is a practical buyer framework drawn from the cases above, not an official universal regulatory standard.
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Questions to ask before buying
- What exact security outcome does the claim promise, and which threats or environments are outside its scope?
- Which test, dataset, or operational evidence supports the claim? Who ran it, when, and under what conditions?
- What was the comparison baseline? Can we review the test method, limitations, and results?
- Does the vendor distinguish a detection result from prevention or real-world protection?
- What information does the product collect, where does that data go, and how does the vendor use or retain it?
- Can the vendor provide independent results and explain false positives, false negatives, and known blind spots?
For comparisons between products, keep the evaluation consistent: compare the same threat and outcome, test recency and conditions, independence and reproducibility, error trade-offs, scope and integration needs, privacy and retention, and total cost and operational burden. A comparison that changes the test conditions or measures different outcomes cannot support a straightforward winner.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.AI adoption figures are not proof of effectiveness
A 2022 SecurityWeek report on an Egress survey of 800 cybersecurity and IT leaders said 77% used products employing AI, while 66% said they understood how AI made security more effective. These are dated survey figures reported by SecurityWeek, not independent verification of product performance; the original survey report and questionnaire were not available in that account. They indicate a reported gap in understanding, not whether AI security tools work. Read SecurityWeek’s account of the Egress survey.
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