Row-level security in CRM Analytics datasets via security predicates — SAQL filter expressions stored on the dataset that apply at query time per running user. Covers the syntax (`'DatasetColumn' operator value`), the `$User.*` context variables, multi-level predicates (role hierarchy + team + region), the performance cost of complex predicates, and the testing discipline (admins bypass predicates by default). NOT for Salesforce Core sharing rules (different runtime), NOT for App / Dashboard / Lens-level access (that's CRM Analytics App sharing, not predicates), NOT for field-level masking inside a dataset (use Encryption + dataset transformations).
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Row-level security in CRM Analytics datasets via security predicates — SAQL filter expressions stored on the dataset that apply at query time per running user. Covers the syntax (`'DatasetColumn' operator value`), the `$User.*` context variables, multi-level predicates (role hierarchy + team + region), the performance cost of complex predicates, and the testing discipline (admins bypass predicates by default). NOT for Salesforce Core sharing rules (different runtime), NOT for App / Dashboard / Lens-level access (that's CRM Analytics App sharing, not predicates), NOT for field-level masking inside a dataset (use Encryption + dataset transformations).
["Dataset(s) the predicate will protect — name and field schema","Access model: owner-only / role-hierarchy / team-membership / region / multi-dimensional","Whether the running user has access to the source SObject (predicate doesn't replace SObject sharing — both apply)","Performance budget for the dashboards that consume this dataset"]
outputs
["Predicate expression string for the dataset","`$User.*` context variables required (and any custom User fields the predicate references)","Test plan covering owner / non-owner / admin / cross-team users","Performance baseline for predicate-protected vs unprotected query"]
dependencies
[]
version
1.0.0
author
Pranav Nagrecha
updated
2026-05-04T00:00:00.000Z
CRM Analytics Security Predicates
Row-level security in CRM Analytics. A security predicate is a SAQL
filter expression stored at the dataset level. Every query that
reads the dataset has the predicate applied automatically, scoped to
the running user's context (Id, role, profile, custom User fields).
The dataset's owner / admin doesn't see it; the running user does.
What this skill is NOT. Salesforce Core SObject sharing rules (Org-Wide
Defaults, role hierarchy, sharing rules, manual sharing, Apex managed
sharing) operate on the source records — predicates operate on the
dataset's copy of those records. Both can apply; they're independent.
App / Dashboard / Lens sharing is different — that's CRM Analytics
Asset sharing (who can open the dashboard at all), not row-level
filtering inside it. Field-level masking is a different control (use
Shield Platform Encryption or transform the dataset to omit / hash the
field).
Before Starting
Confirm the dataset, not the dashboard, is the right level.
Predicates apply to the dataset, not to individual dashboards or
lenses. A predicate set on a dataset filters every dashboard /
lens / SAQL query that reads that dataset. If different audiences
need different filtering, you may need separate datasets — or a
predicate that varies per running user via $User.* rather than
per-dashboard.
Verify SObject-level sharing is also in place. Predicates do
not replace sharing rules on the source records. If a user can't
see an Account in Salesforce Core, the dataflow / recipe that
built the dataset already had access (it ran as the dataflow
user) — the predicate is the only thing keeping the user from
seeing the row in CRM Analytics. That asymmetry is correct, but
surprising.
Plan the test set. At minimum: a row owner, a non-owner with
legitimate access, a non-owner without access, an admin (who
bypasses predicates by default — that's the most common mistake).
Budget for predicate cost. Predicates run on every query.
Complex multi-condition predicates with IN against a large list,
or predicates that reference computed $User.* values, can slow
every dashboard that reads the dataset.
DatasetColumn — the API name of a column in the dataset, in
single quotes. Case-sensitive.
Comparison operators — ==, !=, , , , ,
, (regex).
<
<=
>
>=
in
matches
Logical operators — && (AND), || (OR), parentheses for
grouping.
Values — string literal "...", numeric, list [...], or
a $User.* context variable.
$User.* context variables
Available inside any predicate value position. They resolve at query
time to the running user:
Variable
Resolves to
"$User.Id"
The 18-character Id of the running user
"$User.UserRoleId"
The role Id (or null if no role)
"$User.ProfileId"
The profile Id
"$User.UserName"
The Username
"$User.Email"
Email
"$User.<CustomField__c>"
Any standard or custom User field
The custom-field form is the most useful — define a Region__c on
User, populate per user, then write 'Region' == "$User.Region__c"
to filter the dataset by the running user's region.
The five access patterns and their predicate shapes
CRM Analytics admins (users with the Manage Analytics permission)
see all rows regardless of the predicate. This is intentional — it
prevents admins from being locked out of their own datasets — but
it's the source of the most common testing failure: the team writes
the predicate, the admin tests it, sees all the rows, declares it
working, and discovers in production that non-admin users see what
they expected.
Always test with a non-admin user.
Predicate cost
Predicates run on every query against the dataset. The cost is:
Per-row evaluation of the predicate expression.
Variable substitution — $User.Email requires looking up the
running user's email at query time.
Regex matches — matches is more expensive than == /
in.
Long IN lists — IN ['a', 'b', 'c', ..., 'zzz'] evaluates
each comparison.
For most predicates, the cost is negligible (sub-100ms added). For
predicates with regex matches against role-hierarchy columns over
millions of rows, the cost can be a noticeable fraction of dashboard
load time. Profile in CRM Analytics's query inspector if a
predicate-protected dashboard is slow.
Common Patterns
Pattern A — Owner-only access
When to use. Every user sees only the rows they own. Standard
"my pipeline" / "my cases" semantics.
'OwnerId' == "$User.Id"
Test plan.
User A owns rows 1-10 → sees rows 1-10.
User B owns rows 11-20 → sees rows 11-20.
Admin → sees all rows (bypasses predicate; expected).
Trade. Doesn't surface team-shared visibility. If the user has
been granted access via a sharing rule, they still don't see the row
in the dataset because the predicate is per-dataset, not per-source-row.
For team visibility, use Pattern B.
Pattern B — Owner OR team-member
When to use. Standard sharing-rule equivalent for opportunities
or accounts where team members get visibility.
Prerequisite. The dataset must include a column listing
team-member Ids per row — typically a multi-value column populated by
the dataflow / recipe joining AccountTeamMember against Account.
Test plan.
Owner → sees their owned rows.
Team member → sees rows where they're listed in AccountTeamMembers.
Non-owner / non-team-member → no rows.
Pattern C — Role hierarchy (my role and below)
When to use. Sales managers see rows owned by their reports.
'UserRoleHierarchyIds' matches "$User.UserRoleId"
Prerequisite.UserRoleHierarchyIds is a dataset column that
contains, for each row, the chain of role Ids from the row owner up
to the org top — populated by the dataflow / recipe.
Implementation note. Computing the role chain per row is a
dataflow / recipe responsibility. The predicate is straightforward;
the data preparation is where the cost lives.
Pattern D — Multi-dimensional (region AND team)
When to use. A regional manager sees rows in their region AND on
their team — but not other regions, even if they're on the team.
Trade. Adding dimensions multiplies the test matrix. Each new
condition adds a new dimension of test users.
Pattern E — Service-account bypass for system queries
When to use. A scheduled SAQL query runs as a service user that
must see all rows (export to a warehouse, daily aggregate). The
predicate would block it.
Approach. Grant the service user the Manage Analytics
permission so the predicate is bypassed. Document the bypass: a
service-account-only role + permission set, audit-logged, scope
limited to the relevant dataset(s).
Anti-pattern. Writing ('OwnerId' == "$User.Id" || 'OwnerId' == 'service-user-id') — works but ties the predicate to a specific
user record. If the service user is recreated, the predicate
silently breaks.
Decision Guidance
Situation
Predicate
Reason
Standard "my pipeline" semantics
'OwnerId' == "$User.Id" (Pattern A)
Cleanest; no extra dataset prep
Sharing-rule equivalent (owner or team)
Pattern B with team-member column
Mirrors Salesforce sharing semantics in the dataset
Manager sees their reports
Pattern C with role-hierarchy column
Standard hierarchical access
Region-scoped reporting
'Region' == "$User.Region__c" (custom User field)
Use custom User fields for non-standard dimensions
Multi-dimensional access
Pattern D — combine with &&
Each new dimension multiplies test users
Need different access per dashboard
Either separate datasets or predicate that varies via $User.*
Predicates are per-dataset, not per-dashboard
Service account that needs full access
Manage Analytics permission on the service user
Don't hardcode user Ids in the predicate
Test passes for admin only
Test with a non-admin user
Admins bypass predicates by default
Predicate is slowing dashboards
Profile in CRM Analytics query inspector; consider precomputing fields in the dataset
Per-row evaluation cost adds up
Recommended Workflow
Decide access pattern. Pick from owner-only, owner+team, role-hierarchy, region, multi-dimensional. Start simple; add dimensions only when justified.
Identify the columns the predicate needs. Owner Id is usually present; team-members and role-hierarchy chains are dataflow / recipe-computed columns.
Write the predicate following the syntax in Core Concepts.
Define the test matrix. Owner / non-owner / admin / cross-team — one user per axis. Include a non-admin user; admins bypass predicates.
Apply via the dataset's Security Predicate field (Setup or via the dataset XMD JSON).
Test. Run the same SAQL query as each test user; confirm the row sets match expectations.
Profile. If the dataset feeds latency-sensitive dashboards, run a representative query in the inspector and confirm predicate cost is acceptable.
Review Checklist
Predicate syntax is valid (column names quoted, operators spelled correctly).
Every $User.* reference resolves to a real User field.
If using regex matches, regex is verified against sample data.
Test matrix includes a non-admin user.
Test matrix includes a user with NO access (verifies the predicate actually blocks).
Service-account bypass strategy is documented (if any).
Performance baseline measured (especially if matches is used).
Predicate is stored on the dataset, not duplicated across dashboards.
Salesforce-Specific Gotchas
Admins bypass predicates by default. Test with a non-admin user, always. (See references/gotchas.md § 1.)
Predicates apply to the dataset, not the dashboard. A predicate set on dataset X filters every dashboard that reads dataset X. (See references/gotchas.md § 2.)
Predicates do NOT replace SObject-level sharing on source records. Both layers apply. The dataflow / recipe ran as a privileged user, so the dataset has the data; the predicate is the only filter the running user sees through. (See references/gotchas.md § 3.)
$User.<CustomField__c> resolves at query time — if the custom field is null on the running user, the predicate's right-hand side is null, and 'Column' == null returns no rows for that user. (See references/gotchas.md § 4.)
Role-hierarchy access requires a precomputed column in the dataset. SAQL can't traverse the role hierarchy at query time; the dataflow / recipe must produce a UserRoleHierarchyIds chain per row. (See references/gotchas.md § 5.)
matches regex is more expensive than == and in. Profile if the dashboard is slow. (See references/gotchas.md § 6.)
Hard-coding a service-account user Id in the predicate breaks when the user is recreated. Use the Manage Analytics permission to bypass instead. (See references/gotchas.md § 7.)
Output Artifacts
Artifact
Description
Security predicate expression
The actual string applied to the dataset's Security Predicate field
Dataflow / recipe additions
Any precomputed columns the predicate depends on (team membership, role-hierarchy chain)
Custom User fields used
Per-user values referenced via $User.<CustomField>
Test matrix
Owner / non-owner / admin / cross-team users with expected row sets per user
data/crm-analytics-dataflow-design — building the dataset that the predicate protects; predicate-required columns (team membership, role chain) are dataflow concerns.
data/crm-analytics-app-sharing — different layer: who can open the dashboard / app at all (Asset sharing), not row-level filtering inside it.
security/sharing-architecture — Salesforce Core sharing on the source SObjects; both layers apply when CRM Analytics queries Salesforce data.
security/byok-key-rotation — when the dataset contains Shield-encrypted fields, key rotation interacts with predicate-protected reads.
