Why can wildcard-based membership rules create unintended matches in identity governance queries?

Wildcard logic can broaden matches beyond the intended records because the portal may translate a user-friendly condition into a database pattern search. When percent signs or underscore characters are embedded in the value, the resulting query can match extra characters or sequences. That makes precision and testing essential before relying on the set for governance actions.

Why wildcard membership logic matches more than you expect

Wildcard-based membership rules often behave like pattern-matching queries, not exact equality checks. That means the rule may evaluate a broader set of records than the author intended, especially when the underlying portal translates a simple-looking filter into database wildcard syntax. The result is a membership set that looks precise in the UI but is technically broader underneath.

Identity governance teams usually expect the rule to represent a business definition, such as “records containing this string,” but the query engine may interpret special characters as operators. That mismatch between human intent and machine parsing is the root cause of unintended matches, and it is why test data, escaping rules, and preview results matter before a rule is used for access review or certification.

When you work with identity and access models, it helps to distinguish exact matching from pattern matching. Exact matching is predictable because the stored value must equal the rule value. Pattern matching is flexible, but that flexibility can silently widen scope, which is risky when the resulting set feeds governance actions such as access reviews, ownership assignments, or entitlement cleanup.

How percent signs and underscores change the result set

In many query languages, percent signs and underscores are not ordinary characters. A percent sign can stand for any sequence of characters, while an underscore can stand for a single character. If those symbols appear in the value itself, the query may treat them as wildcards unless they are escaped correctly, so a value that looks specific can become a search pattern.

This is especially important when user-friendly portals hide the actual database expression being generated. A reviewer may think they are filtering on one named group, one role, or one description string, while the backend is actually running a broader pattern search. That can pull in similarly named entries, legacy variants, test objects, or records with slightly different formatting.

The practical issue is not just false positives. Broad matches can also change downstream governance decisions by adding extra records to a certification campaign, excluding the wrong objects from a cleanup task, or creating an inaccurate inventory for policy enforcement. The safer approach is to validate the exact translation, not just the visible form field.

Why precision testing matters before governance actions

Identity governance queries are often used to decide who gets reviewed, removed, or remediated. If the query broadens unexpectedly, the governance action no longer applies to the intended population. That can create noisy reviews, missed remediation, or the accidental inclusion of unrelated identities and entitlements in a control workflow.

Good practice is to test the rule against known examples and edge cases before relying on it. Use values that include special characters, near-matches, and empty or null-like cases so you can see whether the query is matching by exact value or by pattern. A preview that shows the “right” count is not enough unless you also confirm which records are actually in the set.

For teams working in IAM and IGA, the operational question is whether the query is safe enough to drive action. If the answer depends on a hidden translation layer, then the query needs a stricter syntax, escaping, or a different matching method. IAM and IGA Basics is a useful anchor for understanding how governance logic should stay aligned to the actual identity model.

Risk and Threat Considerations

Wildcard logic can create control drift when a governance rule is meant to be exact but is implemented as a pattern search. That increases the chance of overbroad review populations, incorrect entitlement decisions, and inaccurate audit evidence, especially when special characters are present in account names, group names, or descriptions.

Failure mechanism: The portal or query layer interprets wildcard characters as matching operators, so the rule expands beyond the intended records unless the value is escaped and tested against the real backend syntax.

Impact: The resulting set can include unintended identities or miss the intended ones, which weakens access reviews, certification accuracy, and any governance decision that depends on the query output.

Standards & Framework Alignment

This section maps relevant standards and security frameworks to the operational risks and controls described in this guidance.

NIST SP 800-53 Rev 5, NIST CSF 2.0 and OWASP ASVS set the technical controls, while ISO/IEC 27001:2022 defines the regulatory obligations.

Framework	Control / Reference	Relevance
NIST SP 800-53 Rev 5	AC-2 — Account Management	Membership queries affect which accounts or identities enter governance workflows.
	IA-5 — Authenticator Management	Special characters and query handling can affect identity-related control inputs and validation.
Recommendation — Validate query scope before using it to govern account actions. Test identity inputs for metacharacter handling before relying on them.
NIST CSF 2.0	PR.AA-05 — Identity Management, Authentication and Access Control	Wildcard rules can distort access-governance decisions tied to identity scope.
Recommendation — Ensure membership logic matches the intended identity population before enforcement.
ISO/IEC 27001:2022	A.5.15 — Access control	Overbroad membership rules can weaken access control decisions and enforcement scope.
Recommendation — Define and verify access rules so query semantics do not broaden access.
OWASP ASVS	V8 — Authorization	Pattern-based matching can change which entities are authorized or reviewed.
Recommendation — Use exact-match authorization checks when pattern matching would broaden scope.

Practitioner Guidance

What to verify: Confirm whether the platform escapes wildcard characters automatically, and test both literal values and special-character variants before using the rule in production. Validate the actual records returned, not just the count.

Decision rule: If the query language is not explicit about escaping, treat any rule containing percent signs, underscores, or similar metacharacters as high-risk until proven otherwise.

What good looks like: The rule returns only the intended records, the backend syntax is understood by the team, and the preview output matches the final governance population.

Practitioner takeaway: Treat wildcard rules as code, not as plain text, because the real risk is not the character itself but the silent expansion of the governance scope.