[ASM] - Expandr 4735 (#27624)

* Update ranking algorithm for Service Ownership

Currently, we score and return all owners in ${alert.asmserviceowner} in sorted owners; instead, we want ${alert.asmserviceowner} to contain a smaller, high-confidence set of owners that we would be comfortable notifying via email.

Test plan: pytest + manual testing in tenant

* Add release notes

* Verify hyperparameters and update docs

* Add unit test for value-checking in _get_k

* Update release notes

* Manually apply diff in output of pre-commit check: use built-ins for type hints, set generators

---------

Co-authored-by: michal-dagan <109464765+michal-dagan@users.noreply.github.com>

Packs/CortexAttackSurfaceManagement/ReleaseNotes/1_6_21.md

@@ -0,0 +1,5 @@
+#### Scripts
+
+##### RankServiceOwners
+
+- Updated the script to return a high-confidence set of most likely owners based on their relative ranking scores.

Packs/CortexAttackSurfaceManagement/Scripts/RankServiceOwners/RankServiceOwners.py

@@ -5,14 +5,16 @@
 """
 
 
-from typing import Dict, List, Any
+from typing import Any
+from collections.abc import Iterable
 import traceback
 from itertools import groupby
+import math
 
 STRING_DELIMITER = ' | '  # delimiter used for joining Source fields and any additional fields of type string
 
 
-def score(owners: List[Dict[str, Any]]) -> List[Dict[str, Any]]:
+def score(owners: list[dict[str, Any]]) -> list[dict[str, Any]]:
     """
     Owner score is the number of observations on that owner divided by the max number of observations
     for any owner in the list
@@ -27,14 +29,18 @@ def score(owners: List[Dict[str, Any]]) -> List[Dict[str, Any]]:
     return owners
 
 
-def rank(owners: List[Dict[str, Any]]) -> List[Dict[str, Any]]:
+def rank(owners: list[dict[str, Any]]) -> list[dict[str, Any]]:
     """
-    Sort owners by ranking score
+    Sort owners by ranking score and use data-driven algorithm to return the top k,
+    where k is a dynamic value based on the relative scores
+
+    See _get_k for hyperparameters that can be used to adjust the target value of k
     """
-    return sorted(owners, key=lambda x: x['Ranking Score'], reverse=True)
+    k = _get_k(scores=(owner['Ranking Score'] for owner in owners))
+    return sorted(owners, key=lambda x: x['Ranking Score'], reverse=True)[:k]
 
 
-def justify(owners: List[Dict[str, str]]) -> List[Dict[str, str]]:
+def justify(owners: list[dict[str, str]]) -> list[dict[str, str]]:
     """
     For now, `Justification` is the same as `Source`; in the future, will sophisticate
     """
@@ -43,7 +49,7 @@ def justify(owners: List[Dict[str, str]]) -> List[Dict[str, str]]:
     return owners
 
 
-def _canonicalize(owner: Dict[str, Any]) -> Dict[str, Any]:
+def _canonicalize(owner: dict[str, Any]) -> dict[str, Any]:
     """
     Canonicalizes an owner dictionary and adds a deduplication key
     `Canonicalization` whose value is either:
@@ -62,7 +68,7 @@ def _canonicalize(owner: Dict[str, Any]) -> Dict[str, Any]:
     return owner
 
 
-def canonicalize(owners: List[Dict[str, str]]) -> List[Dict[str, Any]]:
+def canonicalize(owners: list[dict[str, str]]) -> list[dict[str, Any]]:
     """
     Calls _canonicalize on each well-formatted owner; drops and logs malformated inputs
     """
@@ -78,7 +84,7 @@ def canonicalize(owners: List[Dict[str, str]]) -> List[Dict[str, Any]]:
     return canonicalized
 
 
-def aggregate(owners: List[Dict[str, str]]) -> List[Dict[str, Any]]:
+def aggregate(owners: list[dict[str, str]]) -> list[dict[str, Any]]:
     """
     Aggregate owners by their canonicalization.
 
@@ -101,7 +107,7 @@ def aggregate(owners: List[Dict[str, str]]) -> List[Dict[str, Any]]:
         name = names[0] if names else ''
         # aggregate Source by union
         source = STRING_DELIMITER.join(sorted(
-            set(owner.get('source', '') for owner in duplicates if owner.get('source', ''))
+            {owner.get('source', '') for owner in duplicates if owner.get('source', '')}
         ))
         # take max Timestamp if there's at least one; else empty string
         timestamps = sorted(
@@ -117,8 +123,8 @@ def aggregate(owners: List[Dict[str, str]]) -> List[Dict[str, Any]]:
         }
 
         # aggregate remaining keys according to type
-        all_keys = set(k for owner in duplicates for k in owner.keys())
-        keys_to_types = {k: type(owner[k]) for owner in duplicates for k in owner.keys()}
+        all_keys = {k for owner in duplicates for k in owner}
+        keys_to_types = {k: type(owner[k]) for owner in duplicates for k in owner}
         other_keys = []
         for key in all_keys:
             if key.lower() not in {'name', 'email', 'source', 'timestamp', 'canonicalization'}:
@@ -127,7 +133,7 @@ def aggregate(owners: List[Dict[str, str]]) -> List[Dict[str, Any]]:
             if keys_to_types[other] == str:
                 # union over strings
                 owner[other] = STRING_DELIMITER.join(sorted(
-                    set(owner.get(other, '') for owner in duplicates if owner.get(other, ''))
+                    {owner.get(other, '') for owner in duplicates if owner.get(other, '')}
                 ))
             elif keys_to_types[other] in (int, float):
                 # max over numerical types
@@ -139,6 +145,63 @@ def aggregate(owners: List[Dict[str, str]]) -> List[Dict[str, Any]]:
     return deduped
 
 
+def _get_k(
+    scores: Iterable[float],
+    target_k: int = 5,
+    k_tol: int = 2,
+    a_tol: float = 1.0,
+    min_score_proportion: float = 0.75
+) -> int:
+    """
+    Return a value of k such that:
+    - target_k >= k <= target_k + k_tol
+    - the top k scores comprise minimum specified proportion of the total score mass
+
+    See unit tests in RankServiceOwners_test.py for a more detailed specification of the
+    expected behavior.
+
+    Notable hyperparameters (which are tuned to target_k=5) and where they come from:
+
+    :param target_k: the value of k we are roughly targeting (set by discussion with PM)
+    :param k_tol: our tolerance for k, or how many additional owners above `target_k` we are willing to show
+        (set by intuition/discussion with PM)
+    :param a_tol: max expected absolute different between two scores in the same "tier"
+        (set by intuition; see unit tests)
+    :param min_score_proportion: the targeted min proportion of the score mass
+        (identified using a gridsearch over values to find best outcome on unit tests)
+    """
+    if target_k < 0:
+        raise ValueError("target_k must be non-negative")
+    if k_tol < 0:
+        raise ValueError("k_tol must be non-negative")
+    if a_tol < 0:
+        raise ValueError("a_tol must be non-negative")
+    if min_score_proportion < 0 or min_score_proportion > 1:
+        raise ValueError("min_score_proportion must be a value between 0 and 1")
+
+    # get up to target_k scores that comprise the desired score proportion
+    scores_desc = sorted(scores, reverse=True)
+    min_score_proportion = sum(scores_desc) * min_score_proportion
+    k = 0
+    cumulative_score = 0.0
+    while cumulative_score < min_score_proportion and k < target_k:
+        cumulative_score += scores_desc[k]
+        k += 1
+
+    # score values are likely groupable into "tiers"; try to find a cutoff between tiers
+    # look for the end of the next element's tier (may be the current or next tier),
+    # where a tier is (arbitrarily) defined by an absolute difference of `a_tol`
+    tier_index = k
+    while tier_index < len(scores_desc) and math.isclose(scores_desc[tier_index], scores_desc[tier_index - 1], abs_tol=a_tol):
+        tier_index += 1
+
+    # add additional score(s) if within tolerance for k
+    if math.isclose(target_k, tier_index, abs_tol=k_tol):
+        k = tier_index
+
+    return k
+
+
 def main():
     try:
         unranked = demisto.args().get("owners", [])

Packs/CortexAttackSurfaceManagement/Scripts/RankServiceOwners/RankServiceOwners_test.py

@@ -1,11 +1,13 @@
 import demistomock as demisto  # noqa: F401
 import pytest
 import unittest
-from RankServiceOwners import score, main, rank, _canonicalize, aggregate
+from RankServiceOwners import score, main, rank, _canonicalize, aggregate, _get_k
+from contextlib import nullcontext as does_not_raise
 
 
 @pytest.mark.parametrize('owners,expected_out', [
     (
+        # returned in sorted order
         [
             {
                 'Name': 'bob', 'Email': 'bob@example.com', 'Source': '',
@@ -27,6 +29,61 @@
             },
         ]
     ),
+    (
+        # wraps one test case from _get_k
+        [
+            {
+                'Name': 'a', 'Email': 'a@example.com', 'Source': '',
+                'Timestamp': '', 'Ranking Score': 10, 'Justification': ''
+            },
+            {
+                'Name': 'b', 'Email': 'b@example.com', 'Source': '',
+                'Timestamp': '', 'Ranking Score': 1, 'Justification': ''
+            },
+            {
+                'Name': 'c', 'Email': 'c@example.com', 'Source': '',
+                'Timestamp': '', 'Ranking Score': 1, 'Justification': ''
+            },
+            {
+                'Name': 'd', 'Email': 'd@example.com', 'Source': '',
+                'Timestamp': '', 'Ranking Score': 1, 'Justification': ''
+            },
+            {
+                'Name': 'e', 'Email': 'e@example.com', 'Source': '',
+                'Timestamp': '', 'Ranking Score': 1, 'Justification': ''
+            },
+            {
+                'Name': 'f', 'Email': 'f@example.com', 'Source': '',
+                'Timestamp': '', 'Ranking Score': 1, 'Justification': ''
+            },
+        ],
+        [
+            {
+                'Name': 'a', 'Email': 'a@example.com', 'Source': '',
+                'Timestamp': '', 'Ranking Score': 10, 'Justification': ''
+            },
+            {
+                'Name': 'b', 'Email': 'b@example.com', 'Source': '',
+                'Timestamp': '', 'Ranking Score': 1, 'Justification': ''
+            },
+            {
+                'Name': 'c', 'Email': 'c@example.com', 'Source': '',
+                'Timestamp': '', 'Ranking Score': 1, 'Justification': ''
+            },
+            {
+                'Name': 'd', 'Email': 'd@example.com', 'Source': '',
+                'Timestamp': '', 'Ranking Score': 1, 'Justification': ''
+            },
+            {
+                'Name': 'e', 'Email': 'e@example.com', 'Source': '',
+                'Timestamp': '', 'Ranking Score': 1, 'Justification': ''
+            },
+            {
+                'Name': 'f', 'Email': 'f@example.com', 'Source': '',
+                'Timestamp': '', 'Ranking Score': 1, 'Justification': ''
+            },
+        ]
+    ),
 ])
 def test_rank(owners, expected_out):
     assert rank(owners) == expected_out
@@ -395,3 +452,76 @@ def test_main(mocker, owners, expected_out, capfd):
     # Verify the output value was set
     expected_calls_to_mock_object = [unittest.mock.call('setAlert', {'asmserviceowner': expected_out})]
     assert demisto_execution_mock.call_args_list == expected_calls_to_mock_object
+
+
+def test_get_k():
+    """
+    These cases are designed to specify the intuition we are trying to implement with the algorithm
+    and verify its default hyperparameters.
+    We assert that if the algorithm matches our intuition at least 80% of the time, it's probably fine.
+
+    See function documentation for explanation of hyperparameters and their defaults.
+    """
+
+    # The first value in each cases is the list of scores output by the model (one per owner)
+    # and the second value is the expected k
+    cases = [
+        # If smallish set of owners, return all or find obvious cutoff
+        ([1], 1),
+        ([1, 1], 2),
+        ([1, 1, 1], 3),
+        ([10, 1, 1], 3),
+        ([1, 1, 1, 1], 4),
+        ([10, 1, 1, 1], 4),
+        ([10, 10, 1, 1], 4),  # or 2; either seems fine
+        ([10, 10, 1, 1], 2),  # or 4; either seems fine
+        ([1, 1, 1, 1, 1], 5),
+        ([10, 1, 1, 1, 1], 5),
+        ([10, 10, 1, 1, 1], 2),
+        ([1, 1, 1, 1, 1, 1], 6),
+        ([1, 1, 1, 1, 1, 1, 1], 7),
+
+        # If larger set of owners, return top handful or find obvious cutoff
+        ([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], 5),
+        ([10, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], 5),
+        ([10, 10, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], 5),  # or 2; either seems fine
+        ([10, 10, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], 2),  # or 5; either seems fine
+        ([10, 10, 10, 1, 1, 1, 1, 1, 1, 1, 1, 1], 3),
+        ([10, 10, 10, 10, 1, 1, 1, 1, 1, 1, 1, 1], 4),
+        ([10, 10, 10, 10, 10, 1, 1, 1, 1, 1, 1, 1], 5),
+        ([100, 10, 10, 10, 10, 1, 1, 1, 1, 1, 1, 1], 5),
+        ([100, 10, 10, 10, 10, 10, 1, 1, 1, 1, 1, 1], 6),
+
+        # Do something reasonable for non-obvious cutoffs
+        ([10, 9, 8, 7, 6, 5, 4, 3, 2, 1], 5),
+        ([19, 17, 15, 13, 11, 9, 7, 5, 3, 1], 5),
+
+        # Do something reasonable for larger scales
+        ([500, 200, 100, 50, 25, 10, 1], 3),
+    ]
+    num_correct = 0
+    for scores, expected_k in cases:
+        if _get_k(scores) == expected_k:
+            num_correct += 1
+
+    assert (num_correct / len(cases)) >= 0.8
+
+
+@pytest.mark.parametrize('target_k, k_tol, a_tol, min_score_proportion, expected_raises', [
+    (-1, 2, 1.0, 0.75, pytest.raises(ValueError, match="target_k must be non-negative")),
+    (5, -1, 1.0, 0.75, pytest.raises(ValueError, match="k_tol must be non-negative")),
+    (5, 2, -1, 0.75, pytest.raises(ValueError, match="a_tol must be non-negative")),
+    (5, 2, 1.0, -1, pytest.raises(ValueError, match="min_score_proportion must be a value between 0 and 1")),
+    (5, 2, 1.0, 1.1, pytest.raises(ValueError, match="min_score_proportion must be a value between 0 and 1")),
+    (5, 2, 1.0, 0.75, does_not_raise()),
+])
+def test_get_k_bad_values(target_k, k_tol, a_tol, min_score_proportion, expected_raises):
+    scores = [1, 1, 1]
+    with expected_raises:
+        assert _get_k(
+            scores,
+            target_k=target_k,
+            k_tol=k_tol,
+            a_tol=a_tol,
+            min_score_proportion=min_score_proportion,
+        )

Packs/CortexAttackSurfaceManagement/pack_metadata.json

@@ -2,7 +2,7 @@
     "name": "Cortex Attack Surface Management",
     "description": "Content for working with Attack Surface Management (ASM).",
     "support": "xsoar",
-    "currentVersion": "1.6.20",
+    "currentVersion": "1.6.21",
     "author": "Cortex XSOAR",
     "url": "https://www.paloaltonetworks.com/cortex",
     "email": "",