Water system controllers don't belong on the internet, says ex-NSA chief after suspected Iran attacks 50%

8/7/2026, 12:53:10 PM

BS Summary: This article contains 26 faulty reasoning types, including Hasty Generalization, Availability Heuristic, and Attempt to Sell a Product or Service, with Representativeness Heuristic as the most egregious example at 28.7% saturation with 119 hits. Analysis detected 974 faulty-reasoning hits from 414 analyzed words, generating a BS Score of 42.6% and a BS Rank of 50% (15,126 of 30,190 articles). This article is better (less manipulative) than 50.10% of the article peer group.

With at least 12 US states’ water systems having been hacked - most likely by Iran - we have to get better at cyber defense, according to retired General and Ex-NSA chief Paul Nakasone, who was speaking to reporters at DEF CON. 
“We have to have higher standards,” Nakasone said. 
“These PLCs should not be connected to the internet.” 
In late July, the FBI said it was investigating attacks conducted by “malicious cyber actors” targeting operational technology devices, including programmable logic controllers (PLCs). 
Iran-linked crews have targeted these devices, which monitor sensor data like tank levels, and can turn pumps on and off, for years. 
Some private-sector security researchers say that they suspect Iranian intruders are behind the recent cyberattacks disrupting water and wastewater facilities. 
“I'd be shocked if it's not Iran,” Halcyon Ransomware Research Center SVP Cynthia Kaiser told The Register at DEF CON on Friday. 
“It's almost certain it's Iran.” 
Neither the FBI nor anyone in the Trump administration, however, has officially blamed Iran. 
Nakasone said he believes that the feds are “taking a measured approach” to attribution. 
“But I see an actor here that has certainly shown a history of being able to do this,” he added, referring to earlier Iranian cyberattacks targeting water facilities’ PLCs. 
“They certainly have the capability,” Nakasone said. 
“There's an intent  we're in conflict with Iran.” 
US water systems present a massive attack surface across disparate facilities that are historically underfunded and have limited IT staff, and sometimes no dedicated cybersecurity employees. 
“We have to think differently about how we defend it,” Nakasone said. 
“Let's talk about the attack surface that we're looking at right now. 
We’ve got 50,000 different water municipalities in the United States, 90 percent of our water comes from these 50,000.” 
Defending these water systems requires partnerships, he added, pointing to DEF CON Franklin, a project launched two years ago at the annual event with hackers volunteering their time and talent to help secure water facilities. 
Nakasone also serves as founding director of Vanderbilt University’s Institute of National Security, and its Wicked Problems Lab. 
He's also working on Project Chimera, a cybersecurity platform being developed by academics and cybersecurity practitioners, and built on open-source technologies to boost critical infrastructure resilience. 
“How do you defend better? 
You defend with a series of partners, in a much more involved approach than we have right now,” Nakasone said. 
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Article reasoning-pattern comparisonThis article: 5.1%The Register: 2.8%Confirmation Bias5.1%This article: 8.9%The Register: 1.0%Anchoring Bias8.9%This article: 21.7%The Register: 2.9%Availability Heuristic21.7%This article: 28.7%The Register: 1.0%Representativeness Heuristic28.7%This article: 0.0%The Register: 1.0%Hindsight Bias0.0%This article: 2.9%The Register: 2.1%Overconfidence Bias2.9%This article: 5.8%The Register: 4.1%Framing Effect5.8%This article: 0.0%The Register: 0.7%Loss Aversion0.0%This article: 11.1%The Register: 0.8%Status Quo Bias11.1%This article: 0.0%The Register: 0.1%Sunk Cost Effect0.0%This article: 6.3%The Register: 2.9%Optimism Bias6.3%This article: 5.3%The Register: 2.5%Pessimism Bias5.3%This article: 10.1%The Register: 7.4%Negativity Bias10.1%This article: 0.0%The Register: 1.5%Self-Serving Bias0.0%This article: 0.0%The Register: 0.7%Fundamental Attribution Error0.0%This article: 0.0%The Register: 0.1%Actor-Observer Bias0.0%This article: 0.0%The Register: 0.3%In-Group Bias0.0%This article: 5.3%The Register: 0.3%Out-Group Homogeneity Bias5.3%This article: 0.0%The Register: 1.1%Halo Effect0.0%This article: 0.0%The Register: 0.1%Horn Effect0.0%This article: 0.0%The Register: 0.0%Dunning-Kruger Effect0.0%This article: 0.0%The Register: 1.6%Recency Bias0.0%This article: 0.0%The Register: 0.2%Primacy Effect0.0%This article: 0.0%The Register: 0.1%Blind-Spot Bias0.0%This article: 0.0%The Register: 0.5%Ad Hominem0.0%This article: 0.0%The Register: 0.2%Straw Man0.0%This article: 7.7%The Register: 3.5%Appeal to Authority7.7%This article: 7.0%The Register: 1.4%False Dilemma7.0%This article: 0.0%The Register: 1.1%Slippery Slope0.0%This article: 0.0%The Register: 0.1%Circular Reasoning0.0%This article: 28.7%The Register: 5.5%Hasty Generalization28.7%This article: 0.0%The Register: 0.3%Red Herring0.0%This article: 0.0%The Register: 0.6%Bandwagon0.0%This article: 5.3%The Register: 2.7%Appeal to Emotion5.3%This article: 2.2%The Register: 0.6%Begging the Question2.2%This article: 12.3%The Register: 1.8%Post Hoc (False Cause)12.3%This article: 0.0%The Register: 0.1%Tu Quoque0.0%This article: 0.0%The Register: 0.7%Burden of Proof0.0%This article: 0.0%The Register: 0.1%Appeal to Nature0.0%This article: 4.6%The Register: 0.3%Composition/Division4.6%This article: 4.8%The Register: 1.8%Anecdotal4.8%This article: 0.0%The Register: 0.0%No True Scotsman0.0%This article: 12.8%The Register: 1.7%Ambiguity (Equivocation)12.8%This article: 0.0%The Register: 0.0%Gambler’s Fallacy0.0%This article: 1.9%The Register: 0.1%Middle Ground1.9%This article: 0.0%The Register: 0.1%Personal Incredulity0.0%This article: 0.0%The Register: 0.2%Special Pleading0.0%This article: 0.0%The Register: 0.2%Genetic Fallacy0.0%This article: 10.6%The Register: 1.8%Unattributed Quote10.6%This article: 5.6%The Register: 1.0%Quote-first Misdirection5.6%This article: 3.6%The Register: 6.7%Biased Writer Voice3.6%This article: 1.9%The Register: 1.3%Indoctrination1.9%This article: 0.0%The Register: 0.1%Politically Left Leaning Bias0.0%This article: 0.0%The Register: 0.1%Politically Right Leaning Bias0.0%This article: 14.7%The Register: 2.1%Attempt to Sell a Product or S…14.7%

414 words analyzed.

Speakers

3speakers51%attributed speech203writer words
Selected voice

Cynthia Kaiser

100%flagged-word coverage
27 attributed words13% of attributed speech100% writer coverage
0%42.5%85.0%Quote-first Misdirection+81.0 ptsWriter: 0.5%Cynthia Kaiser: 81.5%81.5%Attempt to Sell a Product -12.8 ptsWriter: 12.8%Cynthia Kaiser: 0.0%0.0%Unattributed Quote-9.9 ptsWriter: 9.9%Cynthia Kaiser: 0.0%0.0%Biased Writer Voice-7.4 ptsWriter: 7.4%Cynthia Kaiser: 0.0%0.0%

Attribution is sentence-level. Pattern percentages are calculated only from words assigned to that voice.

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Analysis

Hover over highlighted words in the article to view the associated bias or fallacy analysis.