Google has described a second automated system built to catch AI-generated spam in 2026. The new system, the Scaled Abuse Forensics Examiner (SAFE), arrived only months after the company disclosed its first, the Scalable Cluster Termination System (S-CTS). Together, the two systems suggest Google is building layered detection infrastructure aimed at mass-produced AI content. That push comes as publishers struggle to measure how much traffic AI search is sending them.
What SAFE Is Supposed to Do
Google introduced SAFE in a short research paper, about three pages long. The paper says the system is already deployed rather than experimental. 1 Search Engine Journal covered the paper on September 25, 2026, and noted that Google said very little about how SAFE was tested or evaluated. 1
The core idea is that SAFE behaves like a human forensic analyst. Older approaches check content against a fixed list of known violations. SAFE instead tries to replicate the steps of a manual investigation, examining both suspicious content and the accounts behind it. 1 The paper frames the goal as catching material that breaks the spirit of Google's policies, even when no specific written rule covers it. 1
That distinction matters. Rule-based filters are predictable, which makes them easy to game. Spam operators can study the rules, then produce content that technically complies while still being low-value. A system that reasons about intent is harder to reverse-engineer. If SAFE works as described, it closes part of that gap.
S-CTS: The First System of the Year
SAFE builds on S-CTS, which Google described earlier in 2026. 1 According to Google's own research, S-CTS was designed to identify and shut down networks of mass-produced AI spam. 2 Several SEOs have linked it directly to the August spam update, which targeted AI-generated content. 2
The two systems seem to work at different levels:
- S-CTS appears focused on clusters. It finds coordinated networks and terminates them at scale. 2
- SAFE appears focused on investigation. It mimics the judgment a human reviewer would apply to individual cases. 1
Google hasn't said how the two interact or whether SAFE replaces any part of S-CTS. Calling one the successor to the other would overstate what has been disclosed. A more cautious reading is that Google is stacking tools: network-level detection on one side, case-level forensic reasoning on the other.
The Measurement Problem Running in Parallel
The spam crackdown is happening alongside a separate problem for site owners: unreliable analytics for AI-driven traffic. Beyond a one-day GA4 bug on September 1, a nine-month study of more than 51,000 AI Overview events found that 22.4% of AI Overview referral clicks were misattributed to GA4's Direct channel. 2 In one month, that figure reached 29.3%. 2 The result is an ongoing, systematic gap in organic reporting, not a one-off glitch. 2
Put the two developments together and the picture is uncomfortable for publishers. As Search Engine Journal framed it, Google is investing in detection for AI-generated content at the same time analytics tools undercount AI-driven traffic. That leaves publishers without clear visibility into either the risks or the rewards of AI search. 2
Why This Matters for SEO and AEO
For anyone practicing search or answer engine optimization, a few practical conclusions stand out.
Scaled AI content now carries real risk. Two named systems in one year, plus a spam update widely tied to AI content, is a strong signal. Strategies that depend on large volumes of thin, machine-generated pages face steadily rising odds of detection.
Technical compliance is a weaker defense. If SAFE judges content against the intent of Google's guidelines, staying technically within the rules may not protect low-value work. Quality and genuine usefulness become the more durable safeguards.
Your data may understate AI's impact. With roughly a fifth or more of AI Overview clicks potentially landing in Direct, teams should be careful about concluding that AI search isn't sending them traffic. 2 Reported organic declines may also be partly an attribution artifact.
The Takeaway
Google's disclosures remain thin. The SAFE paper is brief, and testing details are sparse. 1 Even so, the direction is clear. Google is moving from list-based enforcement toward systems that try to reason like human investigators, while keeping cluster-level tools for networks.
Publishers face sharper enforcement and blurrier measurement at the same time. The sensible response is to invest in content that holds up under human-style scrutiny, and to audit analytics setups before drawing conclusions about AI search performance.
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