ID Theft Protection AI Search Case Study
How an ID Theft Protection Company Increased Recommendation-Stage Visibility Across Search and AI
How an ID Theft Protection Company Increased Recommendation-Stage Visibility Across Search and AI
Methodology Note:
Directional estimate based on tracked keyword visibility,
combined monthly search volume, and paid search
benchmark value. Not exact attribution.
In just 3 days, using only 25 targeted engagements, this campaign generated an estimated $49,342.17 in monthly branding value. That included $4,446.35 in organic keyword value and $44,895.82 in LLM cited-pages value.
In just 3 days, using only 25 targeted engagements, this campaign generated an estimated $49,342.17 in monthly branding value. That included $4,446.35 in organic keyword value and $44,895.82 in LLM cited-pages value.
Methodology note:
Directional estimate based on tracked keyword visibility, combined monthly search volume, and paid search benchmark value. Not exact attribution.
For ID theft protection brands, speed matters because consumers often start looking for help at the moment concern turns into urgency.
They search for answers, compare protection options, read public guidance, and increasingly rely on AI-generated summaries before deciding which provider feels credible enough to trust. In that environment, efficient visibility gains can influence consideration fast.
This campaign was built around that reality. Rather than spreading effort broadly, CiteWorks Studio concentrated a limited number of targeted engagements on the external sources most likely to shape consumer trust and AI-generated recommendations.
The result was a faster, more efficient expansion of the brand’s visibility across the places where security-minded buyers actually evaluate their options.
Key Outcomes
Achieved in 3 days with only 25 engagements:
15 cited pages
influenced in 5 days
23 high-authority
citation opportunities
activated during
the pilot
235 high-value
keywords ranking
in Google’s top 10
Visibility expanded
across 285 total
keywords
What Changed in the Market
The identity protection journey no longer starts and ends with search rankings. Consumers still search terms such as “best identity theft protection,” “is identity theft protection worth it,” and “what should I do if my identity is stolen,” but increasingly they also rely on public guidance, third-party context, and AI-generated answers to narrow their options.
That shift matters because AI tools often pull from the same external sources people already trust when researching unfamiliar providers.
A brand can perform well in traditional search and still lose visibility at the recommendation stage if it is not well represented in the discussions, reviews, and trusted reference points shaping both consumer judgment and AI output.
In this category, credibility is central to conversion. People want clear information, proof of reliability, and confidence in the provider before taking action.
What the Brand Needed
The brand did not simply need more rankings. It needed stronger representation in the places where trust is formed.
That required improving three decision-stage signals:
Expanding presence in
the environments where
consumers compare
providers and decide
which option feels
most credible
Competitive Visibility
Expanding presence in
the environments where
consumers compare
providers and decide
which option feels
most credible
Competitive Visibility
Improving representation
across the public pages
and discussions AI
systems use when
generating summaries
and recommendations
Citation Strength
Appearing more often in high-intent conversations around identity theft,
fraud alerts, stolen personal data, and protection options
Research Presence
What the Brand Needed
The objective was not just to rank higher. It was to become easier to find, easier to validate, and harder to overlook when buyers were actively weighing their options.
What We Did
Targeted the moments where trust and urgency meet
The campaign focused on page-one discussions where consumers were already looking for answers on identity theft, stolen Social Security numbers, fraud alerts, and protection options. That allowed activity to align with the conversations most likely to influence research behaviour and citation visibility.
Expanded presence across trusted third-party environments
CiteWorks Studio activated a three-surface authority programme spanning an online community forum, a social media platform, and an online review platform. This helped the brand secure prominent visibility in key discussions, appear alongside established cybersecurity and digital safety creators, and strengthen trust through verified review placements.
Measured impact through auditable visibility signals
Performance was tracked through a centralized dashboard monitoring live activation links, keyword targets tied to each placement, positioning, engagement signals, Google page-one context, and LLM visibility linked to brand mentions in AI-generated responses.
“We knew that improving visibility in this space required more than stronger rankings. We needed to be present in the sources people rely on when evaluating protection options, and CiteWorks helped us build that presence in a way we could clearly track.”
— Head of Marketing, ID Theft Protection Brand
The Outcome
The campaign strengthened the brand’s visibility across both search and AI-influenced discovery, especially in the external sources consumers use to evaluate digital safety providers.
By improving presence in trusted security discussions, authority-led content, and third-party review environments, the brand increased visibility for identity protection and fraud-related queries while also expanding its footprint in the sources that shape AI-generated recommendations.
235 high-value
keywords
in Google’s top 10
285 total keywords
where the brand
appeared
15 cited pages
influenced in 5 days
23 high-authority citation
opportunities activated
The result was a stronger foundation for ongoing discovery as more identity theft protection decisions are shaped by a mix of search, public guidance, and AI-generated recommendations.
Want to Understand Your AI Citation Footprint?
We start every engagement with a full audit.
Measurable, Repeatable
Programme
Build a durable foundation
of credible citations that
compounds over time and
continues to influence AI
answers as new queries
emerge.
Citation Architecture
Review
Identify which high-authority
community sources are and
aren't working in your favour
across AI platforms.
Citation Architecture
Review
Identify which high-
authoritycommunity
sources are and
aren't working in your
favour across AI platforms.
AI Visibility Audit
Understand exactly how
LLMs are referencing your
brand today and which
sources are shaping those
answers.
Understanding AI Search Visibility
AI search experiences create answers by pulling information from many places online and then summarizing it into a single response. Large language models like ChatGPT, Gemini, Claude, and Perplexity review signals from websites, articles, and public conversations to respond to questions. The concepts below explain how organizations can track and improve how often they appear inside those AI-generated answers and recommendations.
—————————————————
What Is AI Citation Intelligence?
AI citation intelligence is the process of measuring where AI platforms source their information and how frequently a brand is mentioned or referenced in AI-generated responses. Because LLMs synthesize across multiple sources, the sites and brands that appear repeatedly tend to influence how a topic or company is framed. This practice focuses on identifying which sources shape AI outputs and tracking brand visibility across
different AI systems.
——————————————————
What Is Citation Architecture?
Citation architecture describes the set of sources that consistently inform how AI systems talk about a brand, product, or topic. LLMs draw from websites, articles, forums, and public discussion, and the sources they rely on most often become the backbone of their answers. Building strong citation architecture means ensuring that accurate, credible, high-authority sources are the ones most likely to shape the way AI tools summarize and recommend a brand.
—————————————————
What Is Generative Engine Optimization?
Generative engine optimization (GEO) is the practice of improving the chances that AI systems use and cite your brand or content when generating answers. While traditional SEO is centered on ranking pages in search results, GEO focuses on how LLMs retrieve, interpret, and combine information when responding to a question. The objective is to strengthen the content and sources AI systems rely on, so your brand is treated as a trusted reference in AI responses.
——————————————————
What Is AI Share of Voice?
AI share of voice tracks how often a brand appears in AI-generated answers compared with competitors in the same category. It reflects visibility across AI platforms such as ChatGPT, Gemini, Claude, and Perplexity. Monitoring AI share of voice helps organizations see whether AI systems consistently include and recommend their brand for key queries or whether competitor brands are showing up more often.
About the author
Mark Huntley
Founder and Head of Agency
Mark Huntley, J.D. is the founder of CiteWorks Studio, a strategic advisory focused on visibility, authority, and recommendation presence in AI-shaped search environments. His work centers on embedding-level GEO, vector optimization, and cosine gap engineering — helping brands align their digital presence with the retrieval systems that increasingly shape discovery, interpretation, and choice.
Understanding AI Search Visibility
AI search experiences create answers by pulling information from many places online and then summarizing it into a single response. Large language models like ChatGPT, Gemini, Claude, and Perplexity review signals from websites, articles, and public conversations to respond to questions. The concepts below explain how organizations can track and improve how often they appear inside those AI-generated answers and recommendations.
—————————————————
What Is AI Citation Intelligence?
AI citation intelligence is the process of measuring where AI platforms source their information and how frequently a brand is mentioned or referenced in AI-generated responses. Because LLMs synthesize across multiple sources, the sites and brands that appear repeatedly tend to influence how a topic or
company is framed. This practice focuses on identifying which sources shape AI outputs and tracking brand visibility across different AI systems.
—————————————————
What Is Citation Architecture?
Citation architecture describes the set of sources that consistently inform how AI systems talk about a brand, product, or topic. LLMs draw from websites, articles, forums, and public discussion, and the sources they rely on most often become the backbone of their answers. Building strong citation architecture means ensuring that accurate, credible, high authority sources are the ones most likely to shape the way AI tools summarize and recommend a brand.
—————————————————
What Is Generative Engine Optimization?
Generative engine optimization (GEO) is
the practice of improving the chances that AI systems use and cite your brand or content when generating answers. While traditional SEO is centered on ranking pages in search results, GEO focuses on how LLMs retrieve, interpret,and combine information when responding to a question. The objective is to strengthen the content and sources AI systems rely on, so your brand is treated as a trusted reference in AI responses.
—————————————————
What Is AI Share of Voice?
AI share of voice tracks how often a brand appears in AI-generated answers compared with competitors in the same category. It reflects visibility across AI platforms such as ChatGPT, Gemini, Claude, and Perplexity. Monitoring AI share of voice helps organizations see whether AI systems consistently include and recommend their brand for key queries or whether competitor brands are showing up more often.
About the author
Mark Huntley, J.D. is the founder of CiteWorks Studio, a strategic advisory focused on visibility, authority, and recommendation presence in AI-shaped search environments. His work centers on embedding-level GEO, vector optimization, and cosine gap engineering — helping brands align their digital presence with the retrieval systems that increasingly shape discovery, interpretation, and choice.
Founder and Head of Agency
Mark Huntley
For ID theft protection brands, speed matters because consumers often start looking for help at the moment concern turns into urgency.
They search for answers, compare protection options, read public guidance, and increasingly rely on AI-generated summaries before deciding which provider feels credible enough to trust. In that environment, efficient visibility gains can influence consideration fast.
This campaign was built around that reality. Rather than spreading effort broadly, CiteWorks Studio concentrated a limited number of targeted engagements on the external sources most likely to shape consumer trust and AI-generated recommendations.
The result was a faster, more efficient expansion of the brand’s visibility across the places where security-minded buyers actually evaluate their options.
Key Outcomes
Achieved in 3 days with only 25 engagements:
235 high-value
keywords ranking
in Google’s top 10
Visibility expanded
across 285 total
keywords
15 cited pages
influenced in 5 days
23 high-authority
citation opportunities
activated during
the pilot
What Changed in the Market
The identity protection journey no longer starts and ends with search rankings. Consumers still search terms such as “best identity theft protection,” “is identity theft protection worth it,” and “what should I do if my identity is stolen,” but increasingly they also rely on public guidance, third-party context, and AI-generated answers to narrow their options.
That shift matters because AI tools often pull from the same external sources people already trust when researching unfamiliar providers.
A brand can perform well in traditional search and still lose visibility at the recommendation stage if it is not well represented in the discussions, reviews, and trusted reference points shaping both consumer judgment and AI output.
In this category, credibility is central to conversion. People want clear information, proof of reliability, and confidence in the provider before taking action.
What the Brand Needed
The brand did not simply need more rankings. It needed stronger representation in the places where trust is formed.
That required improving three decision-stage signals:
Appearing more often
in high-intent
conversations around
identity theft,
fraud alerts, stolen
personal data,
and protection options
Research Presence
Improving representation
across the public pages
and discussions AI
systems use when
generating summaries
and recommendations
Citation Strength
Expanding presence in
the environments where
consumers compare
providers and decide
which option feels
most credible
Competitive Visibility
The objective was not just to rank higher. It was to become easier to find, easier to validate, and harder to overlook when buyers were actively weighing their options.
What We Did
Targeted the moments where trust and urgency meet
The campaign focused on page-one discussions where consumers were already looking for answers on identity theft, stolen Social Security numbers, fraud alerts, and protection options. That allowed activity to align with the conversations most likely to influence research behaviour and citation visibility.
Expanded presence across trusted third-party environments
CiteWorks Studio activated a three-surface authority programme spanning an online community forum, a social media platform, and an online review platform. This helped the brand secure prominent visibility in key discussions, appear alongside established cybersecurity and digital safety creators, and strengthen trust through verified review placements.
Measured impact through auditable visibility signals
Performance was tracked through a centralized dashboard monitoring live activation links, keyword targets tied to each placement, positioning, engagement signals, Google page-one context, and LLM visibility linked to brand mentions in AI-generated responses.
“We knew that improving visibility in this space required more than stronger rankings. We needed to be present in the sources people rely on when evaluating protection options, and CiteWorks helped us build that presence in a way we could clearly track.”
— Head of Marketing, ID Theft Protection Brand
The Outcome
The campaign strengthened the brand’s visibility across both search and AI-influenced discovery, especially in the external sources consumers use to evaluate digital safety providers.
By improving presence in trusted security discussions, authority-led content, and third-party review environments, the brand increased visibility for identity protection and fraud-related queries while also expanding its footprint in the sources that shape AI-generated recommendations.
235 high-value
keywords
in Google’s top 10
285 total keywords
where the brand
appeared
15 cited pages
influenced in 5 days
23 high-authority citation
opportunities activated
The result was a stronger foundation for ongoing discovery as more identity theft protection decisions are shaped by a mix of search, public guidance, and AI-generated recommendations.
Want to Understand Your AI Citation Footprint?
We start every engagement with a full audit.
AI Visibility Audit
Understand exactly how
LLMs are referencing your
brand today and which
sources are shaping those
answers.
AI Visibility Audit
Understand exactly how
LLMs are referencing your
brand today and which
sources are shaping those
answers.
Citation Architecture
Review
Identify which high-
authoritycommunity
sources are and
aren't working in your
favour across AI platforms.
Measurable, Repeatable
Programme
Build a durable foundation
of credible citations that
compounds over time and
continues to influence AI
answers as new queries
emerge..
Understanding AI Search Visibility
AI search experiences create answers by pulling information from many places online and then summarizing it into a single response. Large language models like ChatGPT, Gemini, Claude, and Perplexity review signals from websites, articles, and public conversations to respond to questions. The concepts below explain how organizations can track and improve how often they appear inside those AI-generated answers and recommendations.
—————————————————
What Is AI Citation Intelligence?
AI citation intelligence is the process of measuring where AI platforms source their information and how frequently a brand is mentioned or referenced in AI-generated responses. Because LLMs synthesize across multiple sources, the sites and brands that appear repeatedly tend to influence how a topic or
company is framed. This practice focuses on identifying which sources shape AI outputs and tracking brand visibility across different AI systems.
—————————————————
What Is Citation Architecture?
Citation architecture describes the set of sources that consistently inform how AI systems talk about a brand, product, or topic. LLMs draw from websites, articles, forums, and public discussion, and the sources they rely on most often become the backbone of their answers. Building strong citation architecture means ensuring that accurate, credible, high authority sources are the ones most likely to shape the way AI tools summarize and recommend a brand.
—————————————————
What Is Generative Engine Optimization?
Generative engine optimization (GEO) is
the practice of improving the chances that AI systems use and cite your brand or content when generating answers. While traditional SEO is centered on ranking pages in search results, GEO focuses on how LLMs retrieve, interpret,and combine information when responding to a question. The objective is to strengthen the content and sources AI systems rely on, so your brand is treated as a trusted reference in AI responses.
—————————————————
What Is AI Share of Voice?
AI share of voice tracks how often a brand appears in AI-generated answers compared with competitors in the same category. It reflects visibility across AI platforms such as ChatGPT, Gemini, Claude, and Perplexity. Monitoring AI share of voice helps organizations see whether AI systems consistently include and recommend their brand for key queries or whether competitor brands are showing up more often.
About the author
Mark Huntley, J.D. is the founder of CiteWorks Studio, a strategic advisory focused on visibility, authority, and recommendation presence in AI-shaped search environments. His work centers on embedding-level GEO, vector optimization, and cosine gap engineering — helping brands align their digital presence with the retrieval systems that increasingly shape discovery, interpretation, and choice.
Founder and Head of Agency