Data Science for Intelligence Skill

Purpose

This skill provides comprehensive data science methodologies tailored for political intelligence analysis in the Riksdagsmonitor platform. It covers statistical analysis, machine learning, natural language processing, time series forecasting, and network analysis techniques applied to the 6 intelligence frameworks and 82 database views for democratic accountability assessment.

When to Use This Skill

Apply this skill when:

• ✅ Building predictive models for election outcomes or coalition stability

• ✅ Analyzing temporal patterns in voting behavior or policy positions

• ✅ Detecting anomalies in political behavior (sudden voting shifts, absences)

• ✅ Performing text analysis on parliamentary documents and speeches

• ✅ Constructing influence networks from voting alignment patterns

• ✅ Forecasting trends in party support or politician effectiveness

• ✅ Clustering politicians or parties by voting similarity

Do NOT use for:

• ❌ Simple aggregation queries (use SQL views instead)

• ❌ Real-time operational dashboards (use materialized views)

• ❌ Causal inference without proper experimental design or quasi-experimental methods

Data Science Framework for CIA Platform

6 Intelligence Analysis Frameworks

graph TB subgraph "Data Sources" A[Riksdagen API<br/>3.5M votes] B[Election Authority<br/>40 parties] C[World Bank<br/>598K indicators] D[Financial Authority<br/>Agency data] end

subgraph "Data Science Techniques"
    A & B & C & D --> E[Feature Engineering]
    E --> F1[Time Series<br/>Analysis]
    E --> F2[Classification<br/>Models]
    E --> F3[Clustering<br/>Analysis]
    E --> F4[NLP<br/>Processing]
    E --> F5[Network<br/>Analysis]
end

subgraph "Intelligence Frameworks"
    F1 --> G1[1. Temporal<br/>Analysis]
    F2 --> G2[3. Pattern<br/>Recognition]
    F3 --> G3[2. Comparative<br/>Analysis]
    F4 --> G3
    F5 --> G5[5. Network<br/>Analysis]
    F1 & F2 --> G4[4. Predictive<br/>Intelligence]
    G1 & G2 & G3 & G4 & G5 --> G6[6. Decision<br/>Intelligence]
end

subgraph "Intelligence Products"
    G1 & G2 & G3 & G4 & G5 & G6 --> H[Risk Assessments]
    H --> I[Political Scorecards]
    H --> J[Coalition Forecasts]
    H --> K[Anomaly Alerts]
end

style E fill:#ffeb99
style F1 fill:#e1f5ff
style F2 fill:#e1f5ff
style F3 fill:#e1f5ff
style F4 fill:#e1f5ff
style F5 fill:#e1f5ff
style H fill:#ccffcc

1. Time Series Analysis (Temporal Framework)

Purpose: Analyze trends, seasonality, and forecast political metrics over time.

CIA Platform Applications:

• Politician voting participation trends

• Party support trajectories

• Government approval ratings

• Legislative productivity patterns

Decomposition Analysis

Example: Decompose Party Support into Trend, Seasonal, Residual Components

import pandas as pd import numpy as np from statsmodels.tsa.seasonal import seasonal_decompose from statsmodels.tsa.stattools import adfuller import matplotlib.pyplot as plt

class PoliticalTimeSeriesAnalyzer: """ Time series analysis for political intelligence Supports: Temporal Analysis Framework """

def __init__(self, db_connection):
    self.db = db_connection

def decompose_party_support(self, party_code, election_years):
    """
    Decompose historical party support into trend, seasonal, residual
    
    Data Source: sweden_political_party table
    Intelligence Framework: Temporal Analysis
    """
    
    # Query: Historical election results
    query = """
    SELECT 
        election_year,
        percentage as support_percentage
    FROM sweden_political_party
    WHERE party_name = (SELECT party_name FROM sweden_political_party WHERE party_id = %s LIMIT 1)
        AND election_year >= %s
    ORDER BY election_year
    """
    
    df = pd.read_sql(query, self.db, params=[party_code, min(election_years)])
    df['election_year'] = pd.to_datetime(df['election_year'], format='%Y')
    df.set_index('election_year', inplace=True)
    
    # Perform seasonal decomposition (additive model)
    decomposition = seasonal_decompose(
        df['support_percentage'], 
        model='additive', 
        period=3  # 3 elections = 12 years
    )
    
    return {
        'trend': decomposition.trend,
        'seasonal': decomposition.seasonal,
        'residual': decomposition.resid,
        'original': df['support_percentage']
    }

def forecast_arima(self, party_code, forecast_periods=1):
    """
    ARIMA forecasting for next election support
    
    Intelligence Product: Election outcome prediction
    """
    from statsmodels.tsa.arima.model import ARIMA
    
    # Query: Historical support data
    query = """
    SELECT 
        election_year,
        percentage
    FROM sweden_political_party
    WHERE party_name = (SELECT party_name FROM sweden_political_party WHERE party_id = %s LIMIT 1)
    ORDER BY election_year
    """
    
    df = pd.read_sql(query, self.db, params=[party_code])
    
    # Fit ARIMA model (p=1, d=1, q=1 - tune based on ACF/PACF)
    model = ARIMA(df['percentage'], order=(1, 1, 1))
    fitted_model = model.fit()
    
    # Forecast next election(s)
    forecast = fitted_model.forecast(steps=forecast_periods)
    confidence_interval = fitted_model.get_forecast(steps=forecast_periods).conf_int()
    
    return {
        'forecast': forecast,
        'lower_bound': confidence_interval.iloc[:, 0],
        'upper_bound': confidence_interval.iloc[:, 1],
        'model_summary': fitted_model.summary()
    }

def detect_changepoints(self, person_id):
    """
    Detect significant shifts in politician voting behavior
    
    Data Source: view_riksdagen_politician_document_daily_summary
    Intelligence Application: Behavioral anomaly detection
    """
    from ruptures import Pelt
    
    # Query: Daily voting participation rate
    query = """
    SELECT 
        active_date,
        COALESCE(total_document_activity, 0) as activity_count
    FROM view_riksdagen_politician_document_daily_summary
    WHERE person_id = %s
        AND active_date >= CURRENT_DATE - INTERVAL '2 years'
    ORDER BY active_date
    """
    
    df = pd.read_sql(query, self.db, params=[person_id])
    signal = df['activity_count'].values
    
    # Detect changepoints using PELT algorithm
    algo = Pelt(model="rbf").fit(signal)
    changepoints = algo.predict(pen=10)
    
    # Map changepoints to dates
    changepoint_dates = [df.iloc[cp]['active_date'] for cp in changepoints[:-1]]
    
    return {
        'changepoints': changepoints,
        'dates': changepoint_dates,
        'signal': signal
    }

SQL Time Series Query Example:

-- Calculate 12-month rolling average of party voting success rate WITH monthly_performance AS ( SELECT party, DATE_TRUNC('month', vote_date) as month, AVG(CASE WHEN won = TRUE THEN 1.0 ELSE 0.0 END) as win_rate FROM view_riksdagen_party_ballot_support_annual_summary WHERE vote_date >= CURRENT_DATE - INTERVAL '4 years' GROUP BY party, DATE_TRUNC('month', vote_date) ) SELECT party, month, win_rate, AVG(win_rate) OVER ( PARTITION BY party ORDER BY month ROWS BETWEEN 11 PRECEDING AND CURRENT ROW ) as rolling_12m_avg, win_rate - AVG(win_rate) OVER ( PARTITION BY party ORDER BY month ROWS BETWEEN 11 PRECEDING AND CURRENT ROW ) as deviation_from_trend FROM monthly_performance ORDER BY party, month DESC;

2. Classification Models (Pattern Recognition Framework)

Purpose: Classify politicians, parties, or votes into predefined categories based on features.

CIA Platform Applications:

• Predict MP defection risk (HIGH/MEDIUM/LOW)

• Classify voting behavior as LOYAL/INDEPENDENT/REBELLIOUS

• Identify party switcher candidates

• Detect at-risk coalition members

Random Forest Classifier

Example: Predict MP Defection Risk

from sklearn.ensemble import RandomForestClassifier from sklearn.model_selection import train_test_split, cross_val_score from sklearn.metrics import classification_report, confusion_matrix import pandas as pd

class PoliticianRiskClassifier: """ Machine learning classification for behavioral risk assessment Supports: Pattern Recognition Framework """

def __init__(self, db_connection):
    self.db = db_connection
    self.model = None

def prepare_defection_features(self):
    """
    Feature engineering for defection risk prediction
    
    Data Sources:
    - view_riksdagen_politician_summary
    - view_riksdagen_party_coalition_agreeableness
    - view_riksdagen_politician_document_daily_summary
    """
    
    query = """
    WITH politician_metrics AS (
        SELECT 
            p.person_id,
            p.first_name || ' ' || p.last_name as name,
            p.party,
            p.total_days_served,
            p.total_assignments,
            p.total_ballots,
            p.total_documents,
            p.percent_absent,
            p.percent_abstain,
            EXTRACT(YEAR FROM AGE(CURRENT_DATE, p.born)) as age
        FROM view_riksdagen_politician_summary p
        WHERE p.status = 'Tjänstgörande riksdagsledamot'
    ),
    party_cohesion AS (
        SELECT 
            party,
            AVG(party_avg_agreement) as cohesion_score
        FROM view_riksdagen_party_coalition_agreeableness
        GROUP BY party
    ),
    recent_activity AS (
        SELECT 
            person_id,
            AVG(total_document_activity) as avg_monthly_activity,
            STDDEV(total_document_activity) as activity_volatility
        FROM view_riksdagen_politician_document_daily_summary
        WHERE active_date >= CURRENT_DATE - INTERVAL '6 months'
        GROUP BY person_id
    )
    SELECT 
        pm.*,
        pc.cohesion_score as party_cohesion,
        ra.avg_monthly_activity,
        ra.activity_volatility,
        CASE 
            -- Label based on historical defections (ground truth)
            WHEN pm.party != LAG(pm.party) OVER (PARTITION BY pm.person_id ORDER BY pm.total_days_served) 
                THEN 1 
            ELSE 0 
        END as defected_label
    FROM politician_metrics pm
    LEFT JOIN party_cohesion pc ON pm.party = pc.party
    LEFT JOIN recent_activity ra ON pm.person_id = ra.person_id
    """
    
    df = pd.read_sql(query, self.db)
    
    # Feature selection
    features = [
        'total_days_served', 'total_assignments', 'total_ballots', 
        'total_documents', 'percent_absent', 'percent_abstain',
        'age', 'party_cohesion', 'avg_monthly_activity', 'activity_volatility'
    ]
    
    X = df[features].fillna(0)
    y = df['defected_label']
    
    return X, y, df[['person_id', 'name', 'party']]

def train_defection_model(self):
    """
    Train Random Forest classifier for defection risk
    
    Intelligence Product: Risk Assessment - Politician defection probability
    """
    X, y, metadata = self.prepare_defection_features()
    
    # Split data
    X_train, X_test, y_train, y_test = train_test_split(
        X, y, test_size=0.2, random_state=42, stratify=y
    )
    
    # Train model
    self.model = RandomForestClassifier(
        n_estimators=100,
        max_depth=10,
        min_samples_split=5,
        class_weight='balanced',  # Handle imbalanced defection data
        random_state=42
    )
    
    self.model.fit(X_train, y_train)
    
    # Evaluate
    y_pred = self.model.predict(X_test)
    
    # Cross-validation
    cv_scores = cross_val_score(self.model, X, y, cv=5, scoring='f1')
    
    return {
        'accuracy': self.model.score(X_test, y_test),
        'classification_report': classification_report(y_test, y_pred),
        'confusion_matrix': confusion_matrix(y_test, y_pred),
        'cv_f1_mean': cv_scores.mean(),
        'cv_f1_std': cv_scores.std(),
        'feature_importance': dict(zip(X.columns, self.model.feature_importances_))
    }

def predict_defection_risk(self):
    """
    Predict defection risk for all current MPs
    
    Output: Risk scores for each politician
    """
    X, y, metadata = self.prepare_defection_features()
    
    # Predict probabilities
    probabilities = self.model.predict_proba(X)[:, 1]  # Probability of defection
    
    # Create risk report
    risk_report = metadata.copy()
    risk_report['defection_probability'] = probabilities
    risk_report['risk_level'] = pd.cut(
        probabilities,
        bins=[0, 0.3, 0.6, 1.0],
        labels=['LOW', 'MEDIUM', 'HIGH']
    )
    
    return risk_report.sort_values('defection_probability', ascending=False)

3. Clustering Analysis (Comparative Framework)

Purpose: Group similar politicians or parties without predefined labels.

CIA Platform Applications:

• Identify ideological clusters beyond formal party affiliations

• Discover voting blocs within parties

• Group politicians by policy focus

• Detect coalition alignment patterns

K-Means Clustering

Example: Cluster Politicians by Voting Behavior

from sklearn.cluster import KMeans from sklearn.preprocessing import StandardScaler from sklearn.decomposition import PCA import pandas as pd import numpy as np

class PoliticalClusteringAnalyzer: """ Unsupervised clustering for political alignment discovery Supports: Comparative Analysis Framework """

def __init__(self, db_connection):
    self.db = db_connection

def cluster_politicians_by_voting(self, n_clusters=8):
    """
    K-means clustering of politicians based on voting patterns
    
    Data Source: vote_data table
    Intelligence Application: Identify cross-party voting blocs
    """
    
    # Query: Build voting similarity matrix
    query = """
    WITH politician_vote_vectors AS (
        SELECT 
            person_id,
            ballot_id,
            CASE vote 
                WHEN 'Ja' THEN 1
                WHEN 'Nej' THEN -1
                WHEN 'Avstår' THEN 0
                ELSE NULL  -- Exclude absences
            END as vote_value
        FROM vote_data
        WHERE vote_date >= CURRENT_DATE - INTERVAL '12 months'
            AND vote IN ('Ja', 'Nej', 'Avstår')
    ),
    pivot_votes AS (
        SELECT 
            person_id,
            ballot_id,
            vote_value
        FROM politician_vote_vectors
    )
    SELECT 
        pv.person_id,
        p.first_name || ' ' || p.last_name as name,
        p.party,
        -- Aggregate vote patterns (one row per person)
        AVG(CASE WHEN pv.vote_value = 1 THEN 1.0 ELSE 0.0 END) as yes_rate,
        AVG(CASE WHEN pv.vote_value = -1 THEN 1.0 ELSE 0.0 END) as no_rate,
        AVG(CASE WHEN pv.vote_value = 0 THEN 1.0 ELSE 0.0 END) as abstain_rate,
        COUNT(DISTINCT pv.ballot_id) as ballots_participated
    FROM pivot_votes pv
    JOIN person_data p ON pv.person_id = p.person_id
    GROUP BY pv.person_id, p.first_name, p.last_name, p.party
    HAVING COUNT(DISTINCT pv.ballot_id) > 100  -- Active MPs only
    """
    
    df = pd.read_sql(query, self.db)
    
    # Feature matrix
    features = ['yes_rate', 'no_rate', 'abstain_rate', 'ballots_participated']
    X = df[features]
    
    # Standardize features
    scaler = StandardScaler()
    X_scaled = scaler.fit_transform(X)
    
    # K-means clustering
    kmeans = KMeans(n_clusters=n_clusters, random_state=42, n_init=10)
    df['cluster'] = kmeans.fit_predict(X_scaled)
    
    # PCA for visualization
    pca = PCA(n_components=2)
    X_pca = pca.fit_transform(X_scaled)
    df['pca1'] = X_pca[:, 0]
    df['pca2'] = X_pca[:, 1]
    
    return {
        'clustered_data': df,
        'cluster_centers': kmeans.cluster_centers_,
        'inertia': kmeans.inertia_,
        'pca_explained_variance': pca.explained_variance_ratio_
    }

def analyze_cluster_characteristics(self, clustered_data):
    """
    Interpret cluster characteristics for intelligence reporting
    """
    cluster_profiles = {}
    
    for cluster_id in clustered_data['cluster'].unique():
        cluster_members = clustered_data[clustered_data['cluster'] == cluster_id]
        
        cluster_profiles[cluster_id] = {
            'size': len(cluster_members),
            'parties': cluster_members['party'].value_counts().to_dict(),
            'avg_yes_rate': cluster_members['yes_rate'].mean(),
            'avg_no_rate': cluster_members['no_rate'].mean(),
            'avg_abstain_rate': cluster_members['abstain_rate'].mean(),
            'top_members': cluster_members.nlargest(5, 'ballots_participated')[['name', 'party']].to_dict('records')
        }
    
    return cluster_profiles

4. Natural Language Processing (Pattern Recognition Framework)

Purpose: Extract insights from parliamentary documents, speeches, and legislative text.

CIA Platform Applications:

• Topic modeling of parliamentary motions

• Sentiment analysis of debates

• Entity extraction (policy areas, stakeholders)

• Document similarity for coalition positioning

Topic Modeling with LDA

Example: Discover Policy Topics from Parliamentary Motions

from sklearn.feature_extraction.text import CountVectorizer, TfidfVectorizer from sklearn.decomposition import LatentDirichletAllocation import pandas as pd

class ParliamentaryDocumentAnalyzer: """ NLP analysis of parliamentary documents Supports: Pattern Recognition Framework """

def __init__(self, db_connection):
    self.db = db_connection

def extract_topics_from_motions(self, n_topics=10):
    """
    Latent Dirichlet Allocation (LDA) for topic discovery
    
    Data Source: document_element table (document_type = 'mot')
    Intelligence Application: Identify party policy priorities
    """
    
    # Query: Fetch parliamentary motions
    query = """
    SELECT 
        d.document_id,
        d.title,
        d.sub_title,
        d.label as motion_id,
        d.document_type,
        dpr.party,
        EXTRACT(YEAR FROM d.made_date) as year
    FROM document_element d
    JOIN document_person_reference_data dpr ON d.document_id = dpr.document_id
    WHERE d.document_type = 'mot'  -- Parliamentary motions
        AND d.made_date >= CURRENT_DATE - INTERVAL '4 years'
    """
    
    df = pd.read_sql(query, self.db)
    
    # Combine title and subtitle as document text
    df['text'] = df['title'].fillna('') + ' ' + df['sub_title'].fillna('')
    
    # Vectorize documents (Swedish stopwords)
    swedish_stopwords = ['och', 'att', 'det', 'i', 'för', 'på', 'är', 'av', 'som', 'till', 'en', 'den', 'med']
    
    vectorizer = CountVectorizer(
        max_features=1000,
        stop_words=swedish_stopwords,
        ngram_range=(1, 2),  # Unigrams and bigrams
        min_df=5  # Ignore rare terms
    )
    
    doc_term_matrix = vectorizer.fit_transform(df['text'])
    
    # Train LDA model
    lda_model = LatentDirichletAllocation(
        n_components=n_topics,
        max_iter=20,
        learning_method='online',
        random_state=42
    )
    
    lda_output = lda_model.fit_transform(doc_term_matrix)
    
    # Extract top words per topic
    feature_names = vectorizer.get_feature_names_out()
    topics = {}
    
    for topic_idx, topic in enumerate(lda_model.components_):
        top_words_idx = topic.argsort()[-10:][::-1]
        top_words = [feature_names[i] for i in top_words_idx]
        topics[f'Topic_{topic_idx}'] = top_words
    
    # Assign dominant topic to each document
    df['dominant_topic'] = lda_output.argmax(axis=1)
    df['topic_confidence'] = lda_output.max(axis=1)
    
    return {
        'topics': topics,
        'document_topics': df,
        'lda_model': lda_model,
        'vectorizer': vectorizer
    }

def analyze_party_topic_focus(self, document_topics):
    """
    Identify which parties focus on which policy topics
    
    Intelligence Product: Party policy positioning analysis
    """
    party_topic_matrix = document_topics.groupby(['party', 'dominant_topic']).size().unstack(fill_value=0)
    
    # Normalize to percentages
    party_topic_percentage = party_topic_matrix.div(party_topic_matrix.sum(axis=1), axis=0) * 100
    
    return party_topic_percentage

5. Network Analysis (Network Framework)

Purpose: Model relationships between political actors as graphs to identify influence, coalitions, and power structures.

CIA Platform Applications:

• Voting alignment networks (who votes with whom)

• Co-sponsorship networks (legislative collaboration)

• Influence centrality (key power brokers)

• Coalition structure detection

NetworkX Analysis

Example: Voting Alignment Network

import networkx as nx import pandas as pd from scipy.stats import pearsonr

class PoliticalNetworkAnalyzer: """ Graph analysis of political relationships Supports: Network Analysis Framework """

def __init__(self, db_connection):
    self.db = db_connection

def build_voting_alignment_network(self, threshold=0.7):
    """
    Construct weighted graph of politician voting alignment
    
    Data Source: vote_data table
    Edge weight: Pearson correlation of voting patterns
    """
    
    # Query: Pivot vote data into person x ballot matrix
    query = """
    WITH vote_matrix AS (
        SELECT 
            person_id,
            ballot_id,
            CASE vote 
                WHEN 'Ja' THEN 1
                WHEN 'Nej' THEN -1
                ELSE 0
            END as vote_numeric
        FROM vote_data
        WHERE vote_date >= CURRENT_DATE - INTERVAL '12 months'
            AND vote IN ('Ja', 'Nej')
    )
    SELECT 
        vm.person_id,
        vm.ballot_id,
        vm.vote_numeric,
        p.first_name || ' ' || p.last_name as name,
        p.party
    FROM vote_matrix vm
    JOIN person_data p ON vm.person_id = p.person_id
    WHERE p.status = 'Tjänstgörande riksdagsledamot'
    """
    
    df = pd.read_sql(query, self.db)
    
    # Pivot to person x ballot matrix
    vote_pivot = df.pivot_table(
        index='person_id', 
        columns='ballot_id', 
        values='vote_numeric', 
        fill_value=0
    )
    
    # Calculate pairwise voting alignment (Pearson correlation)
    alignment_matrix = vote_pivot.T.corr()
    
    # Build NetworkX graph
    G = nx.Graph()
    
    # Add nodes with attributes
    person_attrs = df[['person_id', 'name', 'party']].drop_duplicates().set_index('person_id')
    for person_id, attrs in person_attrs.iterrows():
        G.add_node(person_id, name=attrs['name'], party=attrs['party'])
    
    # Add edges for high alignment (above threshold)
    for i, person_i in enumerate(alignment_matrix.index):
        for j, person_j in enumerate(alignment_matrix.columns):
            if i < j:  # Avoid duplicates
                alignment = alignment_matrix.loc[person_i, person_j]
                if alignment >= threshold:
                    G.add_edge(person_i, person_j, weight=alignment)
    
    return G

def calculate_network_metrics(self, G):
    """
    Compute centrality metrics for influence assessment
    
    Intelligence Application: Identify key power brokers
    """
    
    # Degree centrality (number of connections)
    degree_centrality = nx.degree_centrality(G)
    
    # Betweenness centrality (bridge between groups)
    betweenness_centrality = nx.betweenness_centrality(G, weight='weight')
    
    # Eigenvector centrality (influence of connections)
    eigenvector_centrality = nx.eigenvector_centrality(G, weight='weight', max_iter=1000)
    
    # PageRank (Google's algorithm adapted)
    pagerank = nx.pagerank(G, weight='weight')
    
    # Compile metrics
    metrics_df = pd.DataFrame({
        'person_id': list(G.nodes()),
        'name': [G.nodes[n]['name'] for n in G.nodes()],
        'party': [G.nodes[n]['party'] for n in G.nodes()],
        'degree_centrality': [degree_centrality[n] for n in G.nodes()],
        'betweenness_centrality': [betweenness_centrality[n] for n in G.nodes()],
        'eigenvector_centrality': [eigenvector_centrality[n] for n in G.nodes()],
        'pagerank': [pagerank[n] for n in G.nodes()]
    })
    
    return metrics_df.sort_values('pagerank', ascending=False)

def detect_communities(self, G):
    """
    Detect voting blocs using community detection algorithms
    
    Intelligence Product: Coalition structure analysis
    """
    from networkx.algorithms import community
    
    # Louvain community detection
    communities = community.greedy_modularity_communities(G, weight='weight')
    
    # Assign community labels
    community_map = {}
    for idx, comm in enumerate(communities):
        for node in comm:
            community_map[node] = idx
    
    nx.set_node_attributes(G, community_map, 'community')
    
    return {
        'communities': communities,
        'modularity': community.modularity(G, communities, weight='weight'),
        'num_communities': len(communities)
    }

ISMS Compliance Mapping

ISO 27001:2022 Controls

A.8.16 - Monitoring Activities

• Machine learning models monitored for performance degradation

• Model retraining schedules documented and executed

A.8.32 - Change Management

• Model version control and deployment processes

• A/B testing for model updates

NIST CSF 2.0 Functions

IDENTIFY (ID)

• ID.RA-1: Asset vulnerabilities identified using predictive models

• ID.RA-3: Threats identified through anomaly detection

DETECT (DE)

• DE.AE-2: Detected events analyzed using ML classification

• DE.CM-4: Malicious code detected (adapted for behavioral anomalies)

CIS Controls v8.1

CIS Control 4: Secure Configuration of Enterprise Assets

• 4.1: Establish secure configuration process for data science pipelines

• Model hyperparameters documented and version-controlled

CIS Control 12: Network Infrastructure Management

• 12.8: Establish and maintain dedicated infrastructure for machine learning workloads

Hack23 ISMS Policy References

Data Classification Policy

• Link: https://github.com/Hack23/ISMS-PUBLIC/blob/main/Data_Classification_Policy.md

• Application: Training data classified, model outputs marked with confidence levels

AI Policy

• Link: https://github.com/Hack23/ISMS-PUBLIC/blob/main/AI_Policy.md

• Application: ML models follow AI governance framework, bias mitigation procedures

Secure Development Policy

• Link: https://github.com/Hack23/ISMS-PUBLIC/blob/main/Secure_Development_Policy.md

• Application: Model development follows SDLC, security testing integrated

References

Official Documentation:

• Scikit-learn: https://scikit-learn.org/

• Statsmodels: https://www.statsmodels.org/

• NetworkX: https://networkx.org/

• NLTK: https://www.nltk.org/

CIA Platform Documentation:

• Data Analysis: DATA_ANALYSIS_INTOP_OSINT.md

• Database Views: DATABASE_VIEW_INTELLIGENCE_CATALOG.md

• Risk Rules: RISK_RULES_INTOP_OSINT.md

Academic Sources:

• "Pattern Recognition and Machine Learning" - Christopher Bishop

• "Introduction to Statistical Learning" - James, Witten, Hastie, Tibshirani

• "Network Science" - Albert-László Barabási