AI доверие и сигурност: измами в Google AI Overviews

Google AI Overviews променя начина, по който хората търсят информация. Вместо познатия списък от сини линкове, много потребители вече виждат AI-генерирано поле с отговор, което изглежда изпипано, уверено и „готово да бъде доверено“. Но когато тези обобщения объркват базови факти като телефонни номера – или още по-лошо, показват контактни данни на измамници – залогът се измества от леко неудобство към реален финансов и поверителностен риск. Тук AI доверие и сигурност (AI trust and safety) спира да е абстрактно понятие и се превръща в ежедневен, спешен проблем.

В тази статия разглеждаме как измамниците вече злоупотребяват с AI Overviews, какво означава това за потребителите и брандовете и как организациите могат да реагират с по-добри практики за AI risk management, AI governance и secure AI deployment.

Ако искате да навлезете по-дълбоко в това как да внедрите AI контролите за риск на ниво организация, разгледайте AI Risk Management Solutions for Businesses на Encorp.ai. Решението автоматизира оценките на риск, централизира доказателства и помага бързо развиващите се AI инициативи да останат в синхрон с изискванията за сигурност и съответствие.

Можете да научите повече и за по-широкото ни AI портфолио на https://encorp.ai.

What are Google’s AI Overviews and why they matter for trust and safety

Google’s AI Overviews are generated answers that sit at the top of some search results. They combine information scraped from multiple web pages with a generative AI model that synthesizes and rephrases content into a single, seemingly authoritative response.

From a user point of view, this can feel like a shortcut: instead of opening several tabs, you get one neat summary with highlighted snippets and follow-up prompts. From an AI trust and safety perspective, however, these same design choices can quietly erode healthy skepticism.

How AI Overviews generate answers (scraping + synthesis)

Under the hood, AI Overviews are powered by large language models (LLMs) that:

Retrieve content from the web relevant to your query.
Predict likely next words based on training data and the retrieved content.
Synthesize an answer that looks coherent and confident.

This is not the same as looking up a verified fact in a structured database. As multiple analyses have pointed out, LLMs are prone to hallucinations—plausible but incorrect statements that sound factual but have no solid grounding in the retrieved data.

For background on LLM behavior and limitations, see:

Why synthesized answers can look authoritative

Several interface and design elements amplify risk:

Prominent placement: Answers appear above organic results, signaling implicit trust.
Single voice: Synthesized text feels like a final verdict, not a collection of sources.
Limited citations: While links are often provided, many users won’t click through.
No visible uncertainty: The system rarely communicates doubt in a clear way.

This makes it easy for incorrect details—like phone numbers or email addresses—to be accepted without question, especially by less technical users or people in a hurry.

The original WIRED article highlighting these risks focuses on exactly this problem: AI-generated answers surfacing fraudulent support numbers that look perfectly legitimate to the average user.

How scammers are exploiting AI Overviews

From a threat-model perspective, scammers don’t need to hack Google to abuse AI Overviews. They can distort the input ecosystem and let generative models do the rest.

This is where AI risk management must extend beyond your own models and infrastructure and consider how third-party AI surfaces information about your brand.

How fake contact data is planted and propagated

Reports from outlets like The Washington Post and Digital Trends describe similar patterns:

Scammers publish fake numbers on low-profile websites, spam directories, or deceptive social profiles, pairing them with the names of well-known companies and banks.
Search indexes pick them up as part of the broader web crawl, treating them as just more content.
AI Overviews retrieve and synthesize this information without strong verification that the number is truly associated with the official brand.
Users see an AI answer that appears curated and vetted, not realizing the underlying data is untrusted and possibly malicious.

Because AI Overviews aggregate from multiple sources, a fake number doesn’t need to dominate traditional SEO rankings. It just needs to appear “relevant enough” to be pulled into the retrieval set the model sees.

Examples of scam numbers and common patterns

Victims have reported scenarios like:

Searching for a bank’s customer support number.
Getting an AI Overview that surfaces a single, highlighted support line.
Calling and being asked for full card numbers, one-time passwords, or remote access to their device.

Credit unions and banks, such as State Department Federal Credit Union, have begun issuing warnings about search-based listing scams, including AI-driven ones.

Patterns to watch for:

Numbers that don’t match those on official bank or brand websites.
Contact details hosted on unrelated domains (e.g., generic blogs or content farms).
Aggressive requests for sensitive data during the call.

Real-world impact: who gets hurt and how

The consequences of these scams go beyond a bad search result. They touch core issues in AI data privacy, AI data security, and enterprise AI security.

Risks for customers (financial, identity theft)

For individual users, the damage can include:

Direct financial loss: Fraudulent transfers, card charges, or drained accounts.
Identity theft: Sharing personal data (address, date of birth, SSN equivalents) that enables long-term identity abuse.
Account takeover: Handing over one-time codes or passwords that grant scammers access to email, banking, and cloud services.

Once this data is leaked, mitigation is slow and painful—credit freezes, dispute processes, password resets, and ongoing monitoring.

Risks for brands and reputational damage

Organizations also face serious exposure:

Trust erosion: Users often blame the brand whose number they thought they were calling, not the search platform.
Support overload: Real support centers get flooded with distressed customers dealing with fraud fallout.
Regulatory scrutiny: In regulated sectors, authorities may still ask what controls the organization had in place to mitigate foreseeable abuse, especially around AI data privacy and user protection.

From a broader enterprise AI security lens, these incidents highlight an uncomfortable reality: your risk surface now includes how external AI systems describe and route users to you, whether you built those systems or not.

How to spot fake contact info and avoid AI-overview scams

Users can’t fix systemic design flaws in AI Overviews, but they can adopt simple habits to reduce risk. Organizations should actively promote these practices to their customers.

Quick verification steps before calling a number

A short checklist can prevent major losses:

Cross-check on the official site
- Open a new tab and go directly to the organization’s website (type the URL manually or use a bookmarked link).
- Confirm the phone number or contact details match what you see in search or AI Overviews.
Use the secure app when available
- For banks and major providers, use in-app secure messaging or the “Contact us” screen.
- Avoid numbers that appear only in search and not in official properties.
Look for HTTPS and domain integrity
- Ensure you’re on a secure site (https://) with the correct domain name (e.g., yourbank.com, not yourbank-support-help.com).
- Double-check spelling and URL structure.
Be skeptical of urgency and overreach
- Legitimate agents rarely demand full card numbers, one-time passwords, or remote access tools to “fix” an issue.
- If a caller pressures you to act immediately, hang up and dial the official number from the company’s site or your card.
Check multiple sources
- Compare results from at least two independent sources: the AI Overview, the organic search results, and the official site.
- If they disagree, trust the official domain—not the AI summary.

For additional consumer-focused guidance, resources from organizations like the Federal Trade Commission and Europol’s Anti-Fraud Centre offer practical anti-scam tips.

Tools and signals to trust (official sites, domain checks, cached pages)

A few extra steps can help more advanced users validate information:

WHOIS and domain age checks: Recently created or obscure domains claiming to be official support portals are red flags.
Search engine cached pages: Use cached versions to see if numbers were recently changed in suspicious ways.
Reputation services: Tools like VirusTotal or browser-integrated protection can flag malicious sites before you engage.

These habits should be part of digital hygiene training for employees as well, especially in finance, healthcare, and critical infrastructure.

What organizations should do to protect users

For businesses, the lesson is clear: AI-shaped search is now part of your threat landscape. You need a structured approach that blends secure AI deployment, AI governance, and robust AI risk management.

Monitor and correct misleading listings

Continuously monitor how you appear in search and AI interfaces
- Regularly search for your brand + “support number,” “customer service,” and key products.
- Document any discrepancies between AI-generated answers and your official contact details.
Harden your official contact footprint
- Keep a single, authoritative page that lists all official contact channels.
- Mark up this page with structured data (Schema.org ContactPoint) so search engines can more reliably identify official numbers.
Rapid incident response
- Define internal playbooks for when scam numbers appear in search or AI Overviews.
- Include responsibilities for security, legal, comms, and customer support.

Design for transparency and provenance

Within your own digital products and AI interfaces:

Make official channels unambiguous: Prominently display “Official support” labels and verified contact details in apps and portals.
Log and trace contact recommendations: If your own AI assistants suggest calling a number or visiting a link, ensure there’s an auditable record of how that recommendation was generated.
Adopt AI governance frameworks: Industry guidelines such as NIST’s AI Risk Management Framework or the OECD AI Principles can help structure policies and controls.

Coordinate with platforms (reporting, takedowns)

Use formal reporting channels: Major platforms provide abuse and misinformation reporting processes. Document and escalate clearly when scam numbers impersonate your brand.
Share evidence with regulators when needed: In high-risk sectors, coordination with national cyber agencies or financial authorities may be appropriate.
Communicate proactively with customers: Publish security advisories explaining how you will and will not contact customers, and how they can verify authenticity.

For many organizations, this requires a more programmatic approach to AI risk—something closer to how they already treat information security or privacy.

What Google and platform operators can do (policy & product fixes)

Responsibility for safe AI experiences is shared. Platforms that operate large-scale AI systems have specific obligations around AI governance and trust.

Provenance and attribution for synthesized answers

To improve safety and transparency, platforms should:

Strengthen source attribution: Make it obvious which domain each piece of critical data (like a phone number) comes from.
Highlight official sources: Visually distinguish data pulled from verified brand domains or government registries.
Show uncertainty: Use interface signals—like warnings or low-confidence labels—when contact details are inferred from weak or conflicting data.

Automated checks and human review for contact info

Contact details are not like movie trivia; they have direct financial and safety implications. Platforms should:

Run verification checks for high-risk entities: Banks, hospitals, government agencies, and utilities should be subject to stricter contact-info verification.
Use anomaly detection: Flag numbers that appear suddenly across many low-quality sites or that contradict well-established official listings.
Enable clear appeals for brands: Provide structured processes for organizations to challenge and correct inaccurate AI-generated information.

Emerging regulations such as the EU AI Act and sectoral guidance from bodies like the European Banking Authority are already pushing platforms toward more rigorous AI risk controls for high-impact use cases.

Moving forward: building AI trust and safety into your strategy

AI-powered interfaces like Google’s AI Overviews are not going away. For users and organizations alike, the solution is not to abandon them, but to embed AI trust and safety thinking into everyday behavior and enterprise strategy.

Key takeaways:

Treat AI-generated answers as starting points, not final truth—especially for contact details and security-sensitive actions.
Educate customers and employees on verification habits: cross-checking with official sites, recognizing red flags, and avoiding risky disclosures.
As an organization, extend your AI risk management to cover third-party AI that mediates how users reach you.
Invest in AI governance and secure AI deployment practices that make provenance, transparency, and escalation part of your default design.

If you are looking to operationalize these ideas—automating risk assessments, tracking AI use cases, and aligning your AI portfolio with regulations and internal policies—consider exploring Encorp.ai’s AI Risk Management Solutions for Businesses. Решението е създадено за предприятия и бързо растящи екипи, които искат да въведат структура, последователност и сигурност в начина, по който внедряват AI.

Чрез комбиниране на по-добри навици на потребителите с контроли на enterprise ниво, организациите могат да намалят вероятността AI удобството да се превърне в нов канал за измами – и вместо това да превърнат AI в надеждна, добре управлявана част от своя дигитален модел.