{"id":7756,"date":"2026-11-12T16:21:15","date_gmt":"2026-11-12T20:21:15","guid":{"rendered":"https:\/\/solutionsreview.com\/identity-management\/?p=7756"},"modified":"2026-11-12T16:21:23","modified_gmt":"2026-11-12T20:21:23","slug":"why-identity-security-buyers-should-make-research-part-of-every-evaluation","status":"publish","type":"post","link":"https:\/\/solutionsreview.com\/identity-management\/why-identity-security-buyers-should-make-research-part-of-every-evaluation\/","title":{"rendered":"Why Identity Security Buyers Should Make Research Part of Every Evaluation"},"content":{"rendered":"

\"Why<\/a><\/p>\n

As part of Solutions Review\u2019s ongoing coverage of the evolving cybersecurity market, our editors explain why identity security buyers should make research, first- and third-party alike, a crucial part of their evaluations.<\/strong><\/em><\/p>\n

Identity security buyers face a difficult task: evaluating products designed to distinguish legitimate access from malicious activity that often looks nearly identical. Vendor demonstrations, reference calls, and product documentation can help explain a solution\u2019s capabilities, but they rarely provide the full picture.<\/p>\n

That challenge has grown as identity security has expanded beyond Single Sign-On (SSO) and multifactor authentication (MFA). Buyers must now assess claims about AI-driven threat detection, automated access governance, and protection for machine and AI agent identities.<\/p>\n

Research studies and reports can help buyers understand those developments and turn broad concerns into specific evaluation criteria. Threat reports, market studies, technical assessments, and deployment findings serve different purposes, but each can inform a purchasing decision when its evidence and limitations are clear. The objective is not to collect more reports. It is to use relevant findings to ask better questions, design stronger evaluations, and identify gaps before committing to a solution.<\/p>\n

Why Identity Security Requires a Research-Informed Approach<\/strong><\/h3>\n

Some security technologies lend themselves to relatively straightforward testing. An endpoint security evaluation, for example, can include known malware samples and simulated attack techniques to assess how a product responds.<\/p>\n

Identity security presents a different challenge. A stolen session token, an unexpected permission change to a service account, or an MFA fatigue attack may involve activity that initially resembles legitimate use. Evaluating a solution\u2019s response requires more than confirming that a feature appears in the interface.<\/p>\n

Reproducing realistic attack conditions safely during a 30-day pilot is also difficult, particularly when production directories and employee accounts are involved. Without a well-defined testing plan, buyers can end up evaluating dashboards and workflows rather than detection effectiveness.<\/p>\n

Environmental complexity adds another layer. Enterprise identity infrastructure<\/a> can span on-premises Active Directory, multiple cloud identity providers, SaaS applications, service accounts, API keys, and AI agents. A clean demonstration environment may not reflect those dependencies or the legacy trust relationships a product will encounter.<\/p>\n

Research can help buyers account for these challenges before testing begins. Threat studies can inform attack scenarios, while deployment reports can highlight integration requirements and operational limitations. Those findings provide a starting point for evaluating what matters in the buyer\u2019s own environment.<\/p>\n

The State of AI-Native Cybersecurity: Moving Beyond Feature Claims<\/strong><\/h3>\n

AI has become a prominent part of identity security messaging<\/a>. Vendors use the term to describe behavioral analytics, risk-based authentication, automated access reviews, and privileged-session anomaly detection.<\/p>\n

For buyers assessing the state of AI-native cybersecurity, the challenge is separating those descriptions from evidence of measurable capability. An “AI-native” designation<\/a> does not, on its own, establish detection quality, governance effectiveness, or reduced administrative effort.<\/p>\n

Research studies and technical reports<\/a> are most useful when they explain what was evaluated and under which conditions. Buyers should look for findings that address questions such as:<\/p>\n