Google Ends Dark Web Monitoring: What to Do Next

Google is shutting down its Dark Web report feature in February 2026. New scans stop on January 15, 2026, and the tool disappears on February 16, 2026. That’s a clean timeline—and an uncomfortable reminder: a security feature can vanish even if the threat doesn’t.

Most teams treated dark web monitoring as a “nice-to-have” alerting layer. The problem is that a lot of organizations quietly let it become a primary signal for identity exposure—especially after Google expanded access beyond Google One subscribers to all account holders. When that signal goes dark, you need a replacement plan that’s stronger than “we’ll just watch for breach headlines.”

This post is part of our AI in Cybersecurity series, and I’m going to take a stance: passive breach visibility is not the same as active risk reduction. If you want fewer account takeovers, fewer helpdesk resets, and fewer “how did they get in?” incidents, you need AI-driven detection and response that connects exposure signals to real actions.

What Google’s shutdown really tells us (and why it matters)

Answer first: Google is sunsetting Dark Web report because it didn’t reliably drive users to clear remediation steps—and that’s a product signal you should treat as a security lesson.

Google’s support note says feedback showed the report “didn’t provide helpful next steps,” and that Google will focus on tools that give clearer, actionable protection. That sounds like a product decision, but it maps to a broader issue in security programs:

• Exposure alerts without context become noise. “Your email is on the dark web” isn’t a plan.
• Identity risk is fast-moving. Data shows up, gets repackaged, enriched, and used in targeted phishing quickly.
• Remediation is fragmented. Users (and enterprises) bounce between password resets, MFA changes, credit freezes, search result removals, and ticket escalations.

If you’re responsible for security operations, this matters because the end of one tool often reveals a hidden dependency: who was watching identity exposure, and what happened after an alert? If the answer is “not much,” then the tool’s retirement may actually be a gift—an excuse to replace passive monitoring with measurable controls.

Dark web monitoring is a lagging indicator

Dark web monitoring tells you that some data exists “out there.” It often doesn’t tell you:

• whether the credentials still work
• whether the account has been targeted
• which apps are at risk (SSO vs non-SSO)
• whether the user is being actively phished
• which privileged roles make this exposure dangerous

For individuals, it’s still useful as an early warning. For enterprises, it’s insufficient unless it feeds a workflow that reduces risk quickly.

The immediate checklist: what to do before February 16, 2026

Answer first: Treat the retirement date like a mini-deadline: export what you can, clean up profiles, and replace the capability with controls that reduce account takeover risk.

Google says it will delete Dark Web report data when the feature is retired. If your organization relied on it for employees (informally or formally), do three things now.

1) Stop assuming you’ll be “notified later”

Once scans stop on January 15, 2026, you’re not getting fresh detections from that feature. If dark web monitoring is part of your risk story—even as a weak signal—your detection coverage drops immediately.

2) Remove and minimize stored monitoring data

If you used the feature personally or for employee guidance, consider deleting monitoring profiles ahead of time rather than letting them sit until retirement. Data minimization is a security control too.

3) Replace “exposure alerts” with “identity outcome” metrics

If you track anything, track outcomes:

• time-to-reset for exposed credentials
• percent of workforce using phishing-resistant MFA (passkeys or hardware keys)
• reduction in successful credential stuffing attempts
• reduction in helpdesk password resets after MFA rollout

These metrics tie directly to business impact and are far harder to argue with than “we monitor the dark web.”

A better replacement: AI-driven identity threat detection (not just alerts)

Answer first: AI-powered security works best here when it connects exposure signals to live behavior—credential misuse, anomalous access, and attacker tooling—then automates response.

When a major platform provider retires a monitoring feature because it lacked “next steps,” the right response isn’t to find a similar alert feed. The better response is to upgrade the whole flow:

1. Detect risk earlier (phishing attempts, credential stuffing, anomalous logins)
2. Confirm risk faster (correlate device, IP reputation, impossible travel, session anomalies)
3. Respond automatically (step-up auth, session revocation, password reset, block rules)

AI helps because modern identity attacks are high-volume and variable. Rule-based detection alone struggles with:

• distributed credential stuffing from residential proxies
• MFA fatigue / push bombing patterns that change daily
• “low and slow” account probing that stays under thresholds
• business email compromise sequences that mimic legitimate behavior

What “AI in cybersecurity” looks like in this use case

AI shouldn’t be a buzzword bolted onto a dashboard. In identity defense, practical AI usually means:

• Anomaly detection on sign-in behavior (time, location, device, app, session duration)
• Risk scoring that prioritizes what’s likely malicious, not just unusual
• Clustering of related events (same spray source hitting many users, same phish kit targeting a department)
• Automated triage that summarizes the incident for analysts (what happened, who is impacted, what to do)

A snippet-worthy way to frame it:

Dark web monitoring tells you your data leaked. AI-driven identity security tells you whether someone is trying to use it—right now.

Passkeys and “Results about you” are helpful—but they’re not the whole story

Answer first: Passkeys reduce phishing risk dramatically, but you still need monitoring for session hijacking, OAuth abuse, and downstream SaaS sprawl.

Google is nudging users toward two alternatives:

• Passkeys for phishing-resistant authentication
• Results about you for removing personal info from Google Search results

I like this direction. Passkeys, in particular, address the most common failure mode: users reusing passwords and getting tricked by phishing pages.

But enterprise security leaders shouldn’t treat passkeys as a cure-all. Here’s what still breaks:

Session theft doesn’t care about your password

If an attacker steals a session token via malware, browser injection, or compromised endpoints, they may bypass login entirely. That’s why you need endpoint visibility, session anomaly detection, and conditional access that can re-check risk mid-session.

OAuth consent phishing is still thriving

Attackers increasingly avoid passwords by tricking users into granting malicious OAuth apps access to email or files. Passkeys don’t stop a user from clicking “Allow.” You need:

• SaaS app allowlists
• continuous monitoring for risky OAuth grants
• automated revocation workflows

Identity sprawl multiplies exposure

Even if Google accounts are strong, employees have hundreds of logins across SaaS tools. If you’re not doing continuous discovery and access governance, you’re defending one door while leaving twenty windows open.

Build a replacement program: from “monitoring” to “prevention + response”

Answer first: The most effective replacement for consumer-style dark web monitoring is a workflow that combines external exposure intelligence, identity security controls, and AI-assisted SOC automation.

Here’s a practical blueprint I’ve found works across mid-market and enterprise environments.

Step 1: Baseline identity protection (30 days)

• Require phishing-resistant MFA for admins first, then expand to high-risk groups
• Disable legacy authentication paths where possible
• Turn on conditional access policies for risky sign-ins
• Enforce password managers for any remaining password-based apps

Step 2: Add exposure + credential misuse signals (30–60 days)

• Monitor for leaked credentials relevant to corporate domains (not just employee personal emails)
• Detect credential stuffing and password spray attempts at the identity provider edge
• Correlate with failed login patterns across critical SaaS apps

Step 3: Automate incident handling with AI (60–90 days)

• Auto-generate incident summaries: user, apps accessed, suspicious IP/device, timeline
• Auto-run containment actions: session revoke, step-up auth, temporary lock, password reset
• Auto-route tickets with the right context to IT/helpdesk to reduce back-and-forth

Step 4: Prove value with simple metrics (ongoing)

Pick 3–5 numbers you can defend:

• mean time to detect suspicious sign-ins
• mean time to contain account takeover attempts
• percentage of workforce on passkeys (or phishing-resistant MFA)
• number of risky OAuth grants revoked
• reduction in successful business email compromise attempts

If your security vendor can’t help you measure these, they’re selling theater.

“People also ask” quick answers

If Google’s dark web monitoring is shutting down, is the dark web risk lower?

No. The shutdown is a product decision. Stolen data marketplaces and credential trading continue regardless of which consumer tool is available.

Is dark web monitoring still worth paying for?

Yes—if it’s part of a workflow that triggers real controls (MFA enforcement, session revocation, conditional access updates). Alone, it’s just awareness.

What’s the fastest way to reduce identity theft and account takeover risk?

Adopt phishing-resistant MFA (passkeys where possible), add detection for suspicious sign-ins, and automate response actions. That combination beats waiting for breach alerts.

Where this fits in the AI in Cybersecurity series

Google retiring Dark Web report is a small headline with a big lesson: security tools that don’t drive action get cut. Attackers don’t have that problem—they iterate constantly.

If you’re using this moment to rethink your approach, aim higher than a replacement alert feed. Build an AI-assisted identity security program that detects misuse in real time and responds automatically. It’s the difference between “we found out later” and “we stopped it.”

If you had to replace one discontinued tool tomorrow, would your program get weaker—or would your controls still catch the same attacks through behavior, anomaly detection, and automated response?