@@ -164,14 +164,14 @@ While both Dag constructors get called when the file is accessed, only ``dag_1``
 
 .. note::
 
-    When searching for Dags inside the Dag bundle, Airflow only considers Python files that contain the strings ``airflow`` and ``dag`` (case-insensitively) as an optimization.
+    When searching for Dags inside the Dag bundle, Airflow only considers Python files that contain ``airflow`` and at least one of ``dag`` or ``asset`` (all matched case-insensitively) as an optimization.
 
     To consider all Python files instead, set the ``[core] dag_discovery_safe_mode`` configuration flag to ``False``. This is the setting to use when your Dags are defined through a wrapper or abstraction whose source does not contain those strings. The flag is read by the Dag file processor, so set it on that component (and on anything that runs ``airflow dags reserialize``) and restart it for the change to take effect -- otherwise Dags may appear after a manual reserialize and then disappear again on the next processor scan.
 
 You can also provide an ``.airflowignore`` file inside your Dag bundle, or any of its subfolders, which describes patterns of files for the loader to ignore. It covers the directory it's in plus all subfolders underneath it. See  :ref:`.airflowignore <concepts:airflowignore>` below for details of the file syntax.
 
 In the case where the ``.airflowignore`` does not meet your needs and you want a more flexible way to control if a python file needs to be parsed by airflow, you can plug your callable by setting ``might_contain_dag_callable`` in the config file.
-Note, this callable will replace the default Airflow heuristic, i.e. checking if the strings ``airflow`` and ``dag`` (case-insensitively) are present in the python file.
+Note, this callable will replace the default Airflow heuristic, i.e. checking if the python file contains ``airflow`` and at least one of ``dag`` or ``asset`` (all matched case-insensitively).
 
 .. code-block::
 
 
@@ -36,10 +36,10 @@ There are very many reasons why your task might not be getting scheduled. Here a
   may want to confirm that this works both where the scheduler runs as well
   as where the worker runs.
 
-- Does the file containing your Dag contain the string ``airflow`` and ``DAG`` somewhere
-  in the contents? When searching the Dag directory, Airflow ignores files not containing
-  ``airflow`` and ``DAG`` in order to prevent the DagBag parsing from importing all python
-  files collocated with user's Dags.
+- Does the file containing your Dag contain ``airflow`` and at least one of ``dag`` or
+  ``asset`` somewhere in the contents (all matched case-insensitively)? When searching the
+  Dag directory, Airflow skips files that do not match, in order to prevent the DagBag
+  parsing from importing all python files collocated with user's Dags.
 
 - Is your ``start_date`` set properly? For time-based Dags, the task won't be triggered until the
   the first schedule interval following the start date has passed.
 
@@ -320,9 +320,10 @@ core:
       default: "True"
     dag_discovery_safe_mode:
       description: |
-        If enabled, Airflow only scans files containing both ``DAG`` and ``airflow`` (case-insensitive)
-        when looking for Dags. Set this to ``False`` to scan every Python file instead -- required when
-        your Dags are defined through a wrapper or abstraction whose source does not contain those strings.
+        If enabled, Airflow only scans files containing ``airflow`` and at least one of ``dag`` or
+        ``asset`` (all matched case-insensitively) when looking for Dags. Set this to ``False`` to scan
+        every Python file instead -- required when your Dags are defined through a wrapper or abstraction
+        whose source does not contain those strings.
 
         This setting is read by the Dag file processor, so it must be configured for that component. In a
         separate-process deployment (for example the Helm chart) set it on the ``dag-processor`` as well as
 
@@ -29,6 +29,7 @@
 from airflow.utils.file import (
     correct_maybe_zipped,
     list_py_file_paths,
+    might_contain_dag_via_default_heuristic,
     open_maybe_zipped,
 )
 
@@ -229,3 +230,22 @@ def test_get_unique_dag_module_name(edge_filename, expected_modification):
         mocked_sha1.return_value.hexdigest.return_value = "mocked_path_hash_sha1"
         modify_module_name = file_utils.get_unique_dag_module_name(edge_filename)
         assert modify_module_name == expected_modification
+
+
+@pytest.mark.parametrize(
+    ("content", "expected"),
+    [
+        pytest.param(b"from airflow.sdk import DAG", True, id="airflow-and-dag"),
+        pytest.param(b"from airflow.sdk import asset", True, id="airflow-and-asset"),
+        pytest.param(b"from airflow.sdk import Asset", True, id="mixed-case-asset"),
+        pytest.param(
+            b"DAG_DEFAULTS = {'asset_bucket': 's3://data-lake'}", False, id="keywords-without-airflow"
+        ),
+        pytest.param(b"from airflow.configuration import conf", False, id="airflow-without-dag-or-asset"),
+    ],
+)
+def test_might_contain_dag_via_default_heuristic(tmp_path, content, expected):
+    candidate = tmp_path / "candidate.py"
+    candidate.write_bytes(content)
+
+    assert might_contain_dag_via_default_heuristic(file_path=str(candidate)) is expected