84 lines
3.1 KiB
Python
84 lines
3.1 KiB
Python
import datetime
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import logging
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import pandas as pd
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from flows.prepPolicy import selectPolicies
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from services.API import AirlockAPIWrapper
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from services.PolicyHandler import getPolicyInfo
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from utils.Selector import Selector
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from utils.utils import colorText, load_env
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logger = logging.getLogger(__name__)
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async def findQuietAgents(api: AirlockAPIWrapper):
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working_dir = load_env("WORKING_DIR")
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selected_policy = await selectPolicies(api, False)
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if selected_policy:
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agents = await api.agents_find_by_group(selected_policy[0].groupid)
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history_days = await Selector.select_value(
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prompt="Enter how many days of history to pull (1–150): ",
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value_type=int,
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valid_range=(1, 150),
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)
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required_quiet = await Selector.select_value(
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prompt="Enter how many days without an untrusted execution before these are considered ready for enforcement? (1–150): ",
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value_type=int,
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valid_range=(1, 150),
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)
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policy_exec_history = await getPolicyInfo(
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api, selected_policy[0], [1, 2, 6, 7], history_days
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)
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if policy_exec_history.empty:
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logging.info("No execution history found for the selected policy and time range.")
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return
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policy_exec_history["datetime"] = pd.to_datetime(
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policy_exec_history["datetime"], format="%Y-%m-%dT%H:%M:%SZ", utc=True
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)
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now = datetime.datetime.now(datetime.timezone.utc)
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policy_exec_history["days_ago"] = policy_exec_history["datetime"].apply(
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lambda dt: (now - dt).days
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)
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hostname_counts = policy_exec_history["hostname"].value_counts()
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agents["execution_count"] = agents["hostname"].map(hostname_counts).fillna(0).astype(int)
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most_recent_exec = policy_exec_history.sort_values(by="days_ago").drop_duplicates(
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subset="hostname", keep="first"
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)
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agents["days_since"] = agents["hostname"].map(
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most_recent_exec.set_index("hostname")["days_ago"]
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)
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agents["required_quiet"] = required_quiet
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agents["enforce_ready"] = agents["days_since"].apply(
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lambda x: True if pd.isna(x) or x > required_quiet else False
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)
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agents = agents.sort_values(by=["execution_count", "hostname"], ascending=[True, True])
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filename = f"{working_dir}\\{selected_policy[0].name}_agents_last_{history_days}_days.csv"
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logging.debug(f"Saving CSV to {filename}")
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print(colorText(f"Saving CSV to {filename}", "green"))
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agents.to_csv(filename, index=False)
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total_agents = len(agents)
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ready_agents = agents["enforce_ready"].sum()
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not_ready_agents = total_agents - ready_agents
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ready_percentage = (ready_agents / total_agents) * 100
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message = (
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f"Total agents: {total_agents}\n"
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f"Agents marked as 'enforce_ready': {ready_agents}\n"
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f"Agents not ready: {not_ready_agents}\n"
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f"Percentage ready for enforcement: {ready_percentage:.2f}%"
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)
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logger.info(message)
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colorText(message, "green") |