Recruiting systems work best when online candidate research is structured, consistent, and legally defensible. The most reliable approach is to set a written policy first, use a repeatable checklist, and record only job relevant findings. In this guide, we cover the two schools of thought on searching candidates online, what information is commonly available on the open web, and how to reduce discrimination risk while still doing due diligence. We also show where StrategyBrain AI Recruiter fits into modern recruiting systems by automating the repetitive LinkedIn outreach and follow up steps, collecting resumes and contact details from interested candidates, and supporting 24/7 multilingual communication, while leaving final qualification decisions to your recruiting team.
Key Takeaways
- Start with policy, not tools: define allowed sources, prohibited data, and documentation rules before anyone searches.
- Job relevance is the filter: only record findings tied to bona fide occupational requirements and role related risk.
- LinkedIn is best for corroboration: use it to validate employment history and professional context, not personal traits.
- Public legal databases require care: common names and incomplete context can create false matches and wasted time.
- Automate outreach, not judgment: StrategyBrain AI Recruiter can handle initial LinkedIn messaging and follow up while recruiters keep final qualification decisions.
- Document consistently: use a standardized note template so your recruiting system is auditable and fair across candidates.
What online candidate research means in recruiting systems
Online candidate research is the practice of reviewing publicly available information about a candidate to clarify questions that come up during screening and interviewing. In recruiting systems, this sits alongside structured interviews, reference checks, and background checks. The key distinction is that online research can surface information that is irrelevant to hiring decisions, including protected characteristics, which is why it needs guardrails.
In this article, “recruiting systems” means the end to end process and tooling used to source, engage, screen, and move candidates through hiring stages. That includes your policies, your ATS, your sourcing channels, and your communication workflows.
Scope boundary: This guide focuses on open web research and LinkedIn workflow implications. It does not replace legal advice, and it does not cover regulated background checks performed by accredited providers.
The two schools of thought and why both matter
When we reviewed the source material and compared it with how modern talent acquisition teams operate, we found the same two viewpoints still show up in 2026. Both are valid, and your recruiting system should acknowledge the tradeoff rather than pretending it does not exist.
Viewpoint 1: due diligence means searching what is available
This view argues that if information is publicly accessible, recruiters and HR teams should consider it, especially when operations leaders expect hiring risk to be managed. The practical motivation is simple: if a serious issue could have been discovered quickly, teams do not want to explain why they ignored it.
Viewpoint 2: searching can increase discrimination risk
This view argues that online searching can expose protected information and create intentional or unintentional bias. It can also uncover details that are not job related and not a bona fide occupational requirement. In other words, the risk is not only what you find, but also what you cannot “unsee” once it is in front of you.
How to reconcile both in a single recruiting system
The reconciliation is a policy first approach: define what sources are allowed, what topics are off limits, and what gets documented. Then train recruiters to follow the same steps for every candidate in the same stage. Consistency is what turns online research from an ad hoc habit into a defensible part of your recruiting system.
What you can find online and what it is actually useful for
The source material lists several common places recruiters look. Below is a structured interpretation for recruiting systems, with a focus on what each source is good for and what to avoid documenting.
Public social profiles (example: Facebook)
Public social profiles can reveal inconsistencies, such as claims about work history that conflict with public posts. They can also reveal a large amount of personal information that is not job related. If your recruiting system allows this category at all, it should be limited to clearly defined risk scenarios and handled by trained reviewers.
- Useful for: verifying obvious contradictions that relate directly to employment claims.
- High risk: exposure to protected characteristics and irrelevant personal details.
- Documentation rule: record only job relevant facts, not personal observations.
LinkedIn is commonly used to corroborate employment history and professional context. The source material also notes an operational detail: candidates can see who viewed their profile, which can prompt them to reach out. That can be helpful, but it can also change candidate behavior in ways your team did not intend.
In our own workflow testing for LinkedIn based recruiting systems, the biggest bottleneck was not profile review. It was the repetitive outreach and follow up required to get to a clear yes or no. That is where StrategyBrain AI Recruiter can fit naturally: it can automatically connect with candidates within your search criteria, introduce the opportunity, answer role and compensation questions, confirm interview interest, and collect resumes and contact information from interested candidates. Recruiters then review the collected resumes and decide who advances.
- Useful for: corroborating employment history and role scope, identifying mutual connections, and understanding professional narrative.
- Operational note: profile views can be visible depending on account settings.
- System improvement: automate initial messaging and follow up to reduce manual workload while keeping human decision making for qualification.
Public legal databases (example: CanLII)
The source material references CanLII as a place to search for convictions or court decisions and notes that common names can make this time consuming. That is a key systems insight: if a method is slow and error prone, it will be applied inconsistently across recruiters and across candidates.
- Useful for: role specific risk checks when legally appropriate and job relevant.
- Limitations: common names increase false matches; context can be incomplete.
- Documentation rule: document the search inputs used and the reason the check was job relevant.
Provincial registries (example: BC Ministry of Justice registry)
The source material describes a provincial registry that can show items ranging from traffic violations to court orders, and it provides navigation guidance such as searching by last name and first name and reviewing “documents” and “charges” sections. It also notes that not all provinces have a similar database and some direct users back to CanLII.
From a recruiting systems perspective, the takeaway is to avoid building a process that depends on one jurisdiction’s tooling unless you can standardize it across all hiring locations. If you cannot standardize it, treat it as an exception process with explicit approval criteria.
Other court registries (example: Manitoba Court of Queen’s Bench)
The source material mentions another registry that is more difficult to navigate but follows the same idea. Difficulty matters because it increases the chance of inconsistent application. If your recruiting system includes this step, define who performs it, when it is triggered, and how results are verified.
A policy first workflow you can standardize
Below is a practical workflow you can implement inside recruiting systems without turning online research into a free for all. We use “protected grounds” as a general term for legally protected characteristics that vary by jurisdiction. Confirm the exact list in the applicable province, state, or country.
Steps
- Define the trigger: specify which stage allows online research, such as after first interview and before offer.
- Define allowed sources: list the exact categories your team may use, such as LinkedIn and specific public legal databases.
- Define prohibited data: explicitly prohibit recording or considering protected grounds and other non job related personal information.
- Use a standardized checklist: require the same checks for every candidate in the same role and stage.
- Document only job relevant findings: store notes in your recruiting system using a consistent template.
- Escalate exceptions: if a finding could affect hiring, route it to HR or legal review rather than leaving it to an individual recruiter.
Copyable documentation template (for your ATS notes)
- Role and requisition: [title], [req id]
- Stage: [stage name], date: [YYYY-MM-DD]
- Sources checked: [LinkedIn], [public legal database], [other allowed source]
- Search inputs: [name used], [location], [other disambiguation]
- Job relevant finding: [fact only, no personal commentary]
- Reason it is job relevant: [tie to job requirement or risk policy]
- Next action: [none], [request clarification], [escalate to HR/legal]
Quick checklist (printable)
- [ ] I am following the written policy for this role and location.
- [ ] I used the same sources and steps used for other candidates at this stage.
- [ ] I avoided collecting or recording protected information.
- [ ] I documented only job relevant facts with a clear rationale.
- [ ] Any potentially adverse decision is escalated for review.
How StrategyBrain AI Recruiter fits into LinkedIn based recruiting systems
Most recruiting software industry stacks treat LinkedIn as a sourcing channel and an inbox problem. Recruiters spend hours connecting, introducing roles, answering repetitive questions, and chasing replies across time zones. That is operationally expensive and it creates inconsistent candidate experience.
StrategyBrain AI Recruiter is designed to automate the initial outreach and qualification conversation on LinkedIn. In practice, that means it can connect with candidates within your targeted search criteria, introduce the job opportunity, learn about each candidate’s situation, answer questions about the role, company, and compensation, confirm interview interest, and collect resumes and contact information from interested candidates. It also supports 24/7 multilingual communication, which helps global hiring teams maintain response speed without adding headcount.
Important boundary: AI Recruiter identifies willingness to communicate or interview, but it does not decide whether a resume fully matches job requirements. Your recruiters still perform final qualification after reviewing the collected resumes. This division of labor is what keeps the system both scalable and accountable.
Where it sits in the process
- Before online research: it can reduce the number of candidates you need to research by quickly confirming interest and collecting resumes.
- During LinkedIn screening: it can standardize the initial messaging so candidates receive consistent information about role, company, compensation, and next steps.
- After interest is confirmed: recruiters can apply the policy first online research workflow to a smaller, higher intent shortlist.
Scaling note for larger teams
If your organization manages multiple LinkedIn accounts, AI Recruiter supports managing more than 100 LinkedIn accounts to build AI powered recruitment teams. That matters when you need to scale outreach volume while keeping messaging quality consistent across recruiters and regions.
Risk controls: discrimination, privacy, and documentation
The source material raises the core risk clearly: online searching can expose protected information and create discrimination risk. Recruiting systems should treat this as a design constraint, not a training footnote.
Controls to build into your recruiting system
- Role based access: limit who can perform online research and who can view the notes.
- Stage gating: only allow online research at a defined stage, not at first glance.
- Standardized notes: require the template so documentation stays factual and job relevant.
- Adverse action review: if a finding could change a hiring decision, require HR review.
- Data minimization: store only what you need for the hiring decision and retention policy.
Security and compliance considerations for AI assisted workflows
If you add AI into recruiting systems, candidates and hiring teams will ask how data is handled. StrategyBrain AI Recruiter states that it complies with privacy regulations in the EU, United States, and Canada, that customer provided data is not used to train AI models, and that LinkedIn account credentials are encrypted and stored independently per user with explicit authorization. Candidate information such as resumes, contact details, and conversation history is described as encrypted, isolated using customer specific keys, and not shared with third parties.
Practical recommendation: include a vendor review step in your recruiting system that verifies these claims in the vendor’s official documentation and your internal security assessment process.
Quick comparison: research methods inside recruiting systems
| Method | Primary purpose | Time cost | Main risk | Best for |
|---|---|---|---|---|
| LinkedIn profile review | Corroborate employment history | Low to medium | Inconsistent interpretation | Most roles, most stages |
| Public social profile review | Clarify contradictions | Medium | Exposure to protected information | Exception based risk checks |
| Public legal database search | Role specific risk review | Medium to high | False matches for common names | Regulated or safety sensitive roles |
| AI assisted LinkedIn outreach (StrategyBrain AI Recruiter) | Automate initial outreach and follow up | Low recruiter time per candidate | Overreliance if boundaries are unclear | High volume sourcing and global hiring |
FAQ
Should recruiting systems include online candidate research at all?
Yes, many teams include it, but only when it is policy driven and consistently applied. The safest approach is to define allowed sources, prohibit recording protected information, and document only job relevant findings.
Is it risky to look at a candidate’s social media?
It can be, because social profiles often reveal protected characteristics and irrelevant personal details. If your recruiting system allows it, restrict it to defined scenarios and require standardized documentation.
What is LinkedIn best used for in candidate research?
LinkedIn is best for corroborating employment history and professional context. It should not be used to infer personal traits that are not job related.
How can we reduce the time spent on LinkedIn recruiting without lowering quality?
Standardize your outreach and follow up workflow and automate the repetitive parts. StrategyBrain AI Recruiter can handle initial LinkedIn connections, introductions, Q&A about the role and compensation, and resume and contact collection, while recruiters keep final qualification decisions.
Does StrategyBrain AI Recruiter decide whether a candidate is qualified?
No. It identifies willingness to communicate or interview and collects resumes and contact details from interested candidates. Recruiters still review resumes and determine fit against job requirements.
What if a public legal database search returns multiple people with the same name?
Treat it as an ambiguous result unless you can disambiguate using job relevant identifiers that your policy allows. Document the search inputs and escalate for review if the finding could affect a hiring decision.
How should we document online research findings in our ATS?
Use a template that captures sources checked, search inputs, a factual job relevant finding, and the reason it matters for the role. Avoid personal commentary and avoid recording protected information.
Can AI assisted recruiting systems be compliant with privacy regulations?
They can be, but compliance depends on the vendor’s controls and your internal governance. Verify claims in official documentation and complete a security and privacy review before deployment.
Conclusion and next steps
Online candidate research can strengthen recruiting systems when it is consistent, job relevant, and documented with care. The practical path is to write the policy first, standardize the checklist, and train recruiters to record only what is relevant to the role. If LinkedIn outreach volume is the bottleneck, consider adding StrategyBrain AI Recruiter to automate initial connections, messaging, follow up, and resume and contact collection, then keep human recruiters responsible for final qualification and hiring decisions.
Next steps: (1) draft your allowed sources and prohibited data policy, (2) implement the ATS note template, and (3) pilot an AI assisted LinkedIn workflow with a single role to measure recruiter time saved and candidate response rates.
Attribution from source material
Original article details preserved for context: “Just Google it! Um, Maybe. Wait… Methods and Implications of Online Candidate Research”, dated November 13, 2014, authored by Kael Campbell, and signed by Ruth Eden, General Manager, Red Seal Recruiting Solutions Ltd. All external links and URL strings from the source have been removed per output requirements.
