diff --git a/rules/S6971/metadata.json b/rules/S6971/metadata.json
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+{
+}
diff --git a/rules/S6971/python/metadata.json b/rules/S6971/python/metadata.json
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+{
+  "title": "Transformers should not be accessed directly when a Scikit-Learn Pipeline uses caching",
+  "type": "BUG",
+  "status": "ready",
+  "remediation": {
+    "func": "Constant\/Issue",
+    "constantCost": "5min"
+  },
+  "tags": [
+  ],
+  "defaultSeverity": "Major",
+  "ruleSpecification": "RSPEC-6971",
+  "sqKey": "S6971",
+  "scope": "All",
+  "defaultQualityProfiles": ["Sonar way"],
+  "quickfix": "targeted",
+  "code": {
+    "impacts": {
+      "MAINTAINABILITY": "MEDIUM",
+      "RELIABILITY": "HIGH"
+    },
+    "attribute": "LOGICAL"
+  }
+}
diff --git a/rules/S6971/python/rule.adoc b/rules/S6971/python/rule.adoc
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+This rule raises an issue when trying to access a Scikit-Learn transformer used in a pipeline with caching directly.
+
+== Why is this an issue?
+
+When using a pipeline with a cache and passing the transformer objects as an instance from a variable, it is possible to access the transformer objects directly.
+
+This is an issue since all the transformers are cloned when the Pipeline is fitted, and therefore, the objects outside the Pipeline are not updated and will yield unexpected results.
+
+== How to fix it
+
+Replace the direct access to the transformer with an access to the `named_steps` attribute of the pipeline.
+
+=== Code examples
+
+==== Noncompliant code example
+
+[source,python,diff-id=1,diff-type=noncompliant]
+----
+from sklearn.datasets import load_diabetes
+from sklearn.preprocessing import RobustScaler
+from sklearn.neighbors import KNeighborsRegressor
+from sklearn.pipeline import Pipeline
+
+diabetes = load_diabetes()
+scaler = RobustScaler()
+knn = KNeighborsRegressor(n_neighbors=5)
+
+pipeline = Pipeline([
+    ('scaler', scaler),
+    ('knn', knn)
+], memory="cache")
+
+pipeline.fit(diabetes.data, diabetes.target)
+print(scaler.center_) # Noncompliant : raises an AttributeError
+
+----
+
+==== Compliant solution
+
+[source,python,diff-id=1,diff-type=compliant]
+----
+from sklearn.datasets import load_diabetes
+from sklearn.preprocessing import RobustScaler
+from sklearn.neighbors import KNeighborsRegressor
+from sklearn.pipeline import Pipeline
+
+diabetes = load_diabetes()
+scaler = RobustScaler()
+knn = KNeighborsRegressor(n_neighbors=5)
+
+pipeline = Pipeline([
+    ('scaler', scaler),
+    ('knn', knn)
+], memory="cache")
+
+pipeline.fit(diabetes.data, diabetes.target)
+print(pipeline.named_steps['scaler'].center_) # Compliant
+----
+
+ifdef::env-github,rspecator-view[]
+
+== Implementation specification
+
+Message : Replace access with `named_steps` attribute of the Pipeline
+Issue location : on the transformer `Name` before the dot
+Secondary location : in the Pipeline array, when giving the object to the Pipeline
+
+Quickfix : possible when the Pipeline is created with the Pipeline constructor. With the make_pipeline, the names are automatically generated, so might be too complicated.
+
+endif::env-github,rspecator-view[]
+
+
+== Resources
+=== Documentation
+
+* Scikit-Learn - Pipelines and composite estimators : https://scikit-learn.org/stable/modules/compose.html#warning:-side-effect-of-caching-transformers[Side effect of caching transformers]