First Round Async. Much work left to do, dont trust results of hash categorization presently.

flows/quietAgent.py

@@ -1,124 +1,84 @@
-#    Copyright (C) 2025 James Brotosky, Brandon Wickline
-#
-#    This program is free software: you can redistribute it and/or modify
-#    it under the terms of the GNU Affero General Public License as published
-#    by the Free Software Foundation, either version 3 of the License, or
-#    (at your option) any later version.
-#
-#    This program is distributed in the hope that it will be useful,
-#    but WITHOUT ANY WARRANTY; without even the implied warranty of
-#    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
-#    GNU Affero General Public License for more details.
-#
-#    You should have received a copy of the GNU Affero General Public License
-#    along with this program.  If not, see <https://www.gnu.org/licenses/>.
-
 import datetime
 import logging
 
-import dotenv
 import pandas as pd
 
 from flows.prepPolicy import selectPolicies
 from services.API import AirlockAPIWrapper
-from services.policyhandler import getPolicyInfo
-from utils.selector import Selector
+from services.PolicyHandler import getPolicyInfo
+from utils.Selector import Selector
 from utils.utils import colorText, load_env
 
 logger = logging.getLogger(__name__)
 
 
-dotenv.load_dotenv()
-
-
-def findQuietAgents(api: AirlockAPIWrapper):
+async def findQuietAgents(api: AirlockAPIWrapper):
     working_dir = load_env("WORKING_DIR")
-    # Get policy selection and agent list
-    selected_policy = selectPolicies(api, False)
-    if selected_policy:
-        agents = api.agents_find_by_group(selected_policy[0].groupid)
+    selected_policy = await selectPolicies(api, False)
 
-        # Prompt user for history range
-        history_days = Selector.select_value(
+    if selected_policy:
+        agents = await api.agents_find_by_group(selected_policy[0].groupid)
+
+        history_days = await Selector.select_value(
             prompt="Enter how many days of history to pull (1–150): ",
             value_type=int,
             valid_range=(1, 150),
         )
-        required_quiet = Selector.select_value(
-            prompt="Enter how many days without an untrusted execution before these are considered ready for enforcement? (1–365): ",
+        required_quiet = await Selector.select_value(
+            prompt="Enter how many days without an untrusted execution before these are considered ready for enforcement? (1–150): ",
             value_type=int,
             valid_range=(1, 150),
         )
 
-        # Get execution history as a DataFrame
-        policy_exec_history = getPolicyInfo(
+        policy_exec_history = await getPolicyInfo(
             api, selected_policy[0], [1, 2, 6, 7], history_days
         )
-        
-       
+
         if policy_exec_history.empty:
             logging.info("No execution history found for the selected policy and time range.")
             return
 
-
-        # Convert 'datetime' column to timezone-aware datetime objects
         policy_exec_history["datetime"] = pd.to_datetime(
             policy_exec_history["datetime"], format="%Y-%m-%dT%H:%M:%SZ", utc=True
         )
 
-        # Get current UTC time
         now = datetime.datetime.now(datetime.timezone.utc)
-
-        # Calculate days ago
         policy_exec_history["days_ago"] = policy_exec_history["datetime"].apply(
             lambda dt: (now - dt).days
         )
 
-        # Count total executions per hostname
         hostname_counts = policy_exec_history["hostname"].value_counts()
-
-        # Map execution counts to agents
         agents["execution_count"] = agents["hostname"].map(hostname_counts).fillna(0).astype(int)
 
-        # Find most recent execution per hostname
         most_recent_exec = policy_exec_history.sort_values(by="days_ago").drop_duplicates(
             subset="hostname", keep="first"
         )
-
-        # Map most recent execution age to agents
         agents["days_since"] = agents["hostname"].map(
             most_recent_exec.set_index("hostname")["days_ago"]
         )
 
-        # Check for enforcement readiness
         agents["required_quiet"] = required_quiet
         agents["enforce_ready"] = agents["days_since"].apply(
             lambda x: True if pd.isna(x) or x > required_quiet else False
         )
 
-        # Sort agents by execution count and hostname
         agents = agents.sort_values(by=["execution_count", "hostname"], ascending=[True, True])
 
-        # Save to CSV
         filename = f"{working_dir}\\{selected_policy[0].name}_agents_last_{history_days}_days.csv"
         logging.debug(f"Saving CSV to {filename}")
         print(colorText(f"Saving CSV to {filename}", "green"))
         agents.to_csv(filename, index=False)
 
-        # Summary statistics
         total_agents = len(agents)
         ready_agents = agents["enforce_ready"].sum()
         not_ready_agents = total_agents - ready_agents
         ready_percentage = (ready_agents / total_agents) * 100
 
-        # Print results
-        
-    
         message = (
             f"Total agents: {total_agents}\n"
             f"Agents marked as 'enforce_ready': {ready_agents}\n"
             f"Agents not ready: {not_ready_agents}\n"
             f"Percentage ready for enforcement: {ready_percentage:.2f}%"
         )
-        logger.debug(message)
-        colorText(message,"green")
+        logger.info(message)
+        colorText(message, "green")