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https://github.com/prowler-cloud/prowler.git
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feat(prowler-threatscore): add Weight field inside req (#7795)
Co-authored-by: Sergio Garcia <hello@mistercloudsec.com>
This commit is contained in:
Pedro Martín
@@ -651,58 +651,114 @@ def get_table(current_compliance, table):
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def get_threatscore_mean_by_pillar(df):
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modified_df = df[df["STATUS"] == "FAIL"]
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score_per_pillar = {}
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max_score_per_pillar = {}
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modified_df["REQUIREMENTS_ATTRIBUTES_LEVELOFRISK"] = pd.to_numeric(
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modified_df["REQUIREMENTS_ATTRIBUTES_LEVELOFRISK"], errors="coerce"
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)
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for _, row in df.iterrows():
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pillar = (
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row["REQUIREMENTS_ATTRIBUTES_SECTION"].split(" - ")[0]
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if isinstance(row["REQUIREMENTS_ATTRIBUTES_SECTION"], str)
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else "Unknown"
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)
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pillar_means = (
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modified_df.groupby("REQUIREMENTS_ATTRIBUTES_SECTION")[
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"REQUIREMENTS_ATTRIBUTES_LEVELOFRISK"
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]
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.mean()
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.round(2)
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)
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if pillar not in score_per_pillar:
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score_per_pillar[pillar] = 0
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max_score_per_pillar[pillar] = 0
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level_of_risk = pd.to_numeric(
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row["REQUIREMENTS_ATTRIBUTES_LEVELOFRISK"], errors="coerce"
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)
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level_of_risk = 1 if pd.isna(level_of_risk) else level_of_risk
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weight = 1
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if "REQUIREMENTS_ATTRIBUTES_WEIGHT" in row and not pd.isna(
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row["REQUIREMENTS_ATTRIBUTES_WEIGHT"]
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):
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weight = pd.to_numeric(
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row["REQUIREMENTS_ATTRIBUTES_WEIGHT"], errors="coerce"
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)
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weight = 1 if pd.isna(weight) else weight
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max_score_per_pillar[pillar] += level_of_risk * weight
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if row["STATUS"] == "PASS":
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score_per_pillar[pillar] += level_of_risk * weight
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output = []
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for pillar, mean in pillar_means.items():
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output.append(f"{pillar} - [{mean}]")
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for pillar in max_score_per_pillar:
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risk_score = 0
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if max_score_per_pillar[pillar] > 0:
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risk_score = (score_per_pillar[pillar] / max_score_per_pillar[pillar]) * 100
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output.append(f"{pillar} - [{risk_score:.1f}%]")
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for value in output:
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if value.split(" - ")[0] in df["REQUIREMENTS_ATTRIBUTES_SECTION"].values:
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base_pillar = value.split(" - ")[0]
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if base_pillar in df["REQUIREMENTS_ATTRIBUTES_SECTION"].values:
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df.loc[
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df["REQUIREMENTS_ATTRIBUTES_SECTION"] == value.split(" - ")[0],
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df["REQUIREMENTS_ATTRIBUTES_SECTION"] == base_pillar,
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"REQUIREMENTS_ATTRIBUTES_SECTION",
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] = value
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return df
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def get_table_prowler_threatscore(df):
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df = df[df["STATUS"] == "FAIL"]
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score_per_pillar = {}
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max_score_per_pillar = {}
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pillars = {}
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# Delete " - " from the column REQUIREMENTS_ATTRIBUTES_SECTION
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df["REQUIREMENTS_ATTRIBUTES_SECTION"] = (
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df["REQUIREMENTS_ATTRIBUTES_SECTION"].str.split(" - ").str[0]
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)
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df_copy = df.copy()
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df["REQUIREMENTS_ATTRIBUTES_LEVELOFRISK"] = pd.to_numeric(
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df["REQUIREMENTS_ATTRIBUTES_LEVELOFRISK"], errors="coerce"
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)
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score_df = (
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df.groupby("REQUIREMENTS_ATTRIBUTES_SECTION")[
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"REQUIREMENTS_ATTRIBUTES_LEVELOFRISK"
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]
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.mean()
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.reset_index()
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.rename(
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columns={
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"REQUIREMENTS_ATTRIBUTES_SECTION": "Pillar",
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"REQUIREMENTS_ATTRIBUTES_LEVELOFRISK": "Score",
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}
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for _, row in df_copy.iterrows():
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pillar = (
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row["REQUIREMENTS_ATTRIBUTES_SECTION"].split(" - ")[0]
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if isinstance(row["REQUIREMENTS_ATTRIBUTES_SECTION"], str)
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else "Unknown"
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)
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)
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if pillar not in pillars:
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pillars[pillar] = {"FAIL": 0, "PASS": 0, "MUTED": 0}
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score_per_pillar[pillar] = 0
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max_score_per_pillar[pillar] = 0
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level_of_risk = pd.to_numeric(
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row["REQUIREMENTS_ATTRIBUTES_LEVELOFRISK"], errors="coerce"
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)
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level_of_risk = 1 if pd.isna(level_of_risk) else level_of_risk
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weight = 1
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if "REQUIREMENTS_ATTRIBUTES_WEIGHT" in row and not pd.isna(
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row["REQUIREMENTS_ATTRIBUTES_WEIGHT"]
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):
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weight = pd.to_numeric(
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row["REQUIREMENTS_ATTRIBUTES_WEIGHT"], errors="coerce"
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)
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weight = 1 if pd.isna(weight) else weight
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max_score_per_pillar[pillar] += level_of_risk * weight
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if row["STATUS"] == "PASS":
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pillars[pillar]["PASS"] += 1
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score_per_pillar[pillar] += level_of_risk * weight
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elif row["STATUS"] == "FAIL":
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pillars[pillar]["FAIL"] += 1
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if "MUTED" in row and row["MUTED"] == "True":
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pillars[pillar]["MUTED"] += 1
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result_df = []
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for pillar in pillars.keys():
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risk_score = 0
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if max_score_per_pillar[pillar] > 0:
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risk_score = (score_per_pillar[pillar] / max_score_per_pillar[pillar]) * 100
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result_df.append({"Pillar": pillar, "Score": risk_score})
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score_df = pd.DataFrame(result_df)
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score_df = score_df.sort_values("Score", ascending=True)
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fig = px.bar(
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score_df,
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@@ -710,22 +766,25 @@ def get_table_prowler_threatscore(df):
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y="Score",
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color="Score",
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color_continuous_scale=[
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"#45cc6e",
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"#f4d44d",
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"#e77676",
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], # verde → amarillo → rojo
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hover_data={"Score": True, "Pillar": True},
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labels={"Score": "Average Risk Score", "Pillar": "Section"},
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"#f4d44d",
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"#45cc6e",
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],
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labels={"Score": "Risk Score (%)", "Pillar": "Section"},
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height=400,
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text="Score",
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)
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fig.update_traces(texttemplate="%{text:.1f}%", textposition="outside")
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fig.update_layout(
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xaxis_title="Pillar",
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yaxis_title="Level of Risk",
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yaxis_title="Risk Score (%)",
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margin=dict(l=20, r=20, t=30, b=20),
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plot_bgcolor="rgba(0,0,0,0)",
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paper_bgcolor="rgba(0,0,0,0)",
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coloraxis_colorbar=dict(title="Risk"),
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coloraxis_colorbar=dict(title="Risk %"),
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yaxis=dict(range=[0, 110]),
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)
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return dcc.Graph(
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