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SOX Compliance and Production Change Approvals: Real vs. “Rubber Stamp”

Internal Control Expectations for Change Management

Under Sarbanes-Oxley (SOX) Section 404, companies must maintain effective internal control over financial reporting – which includes IT general controls like change management. Both management and auditors (guided by PCAOB/AICPA standards) evaluate whether changes to production systems (especially those impacting financial data) are properly controlled. In practice, change management is expected to be “a systematic and standardised approach to ensuring all changes to the IT environment are appropriate, authorised and preserve the integrity of… programs and data”. The widely-used COSO internal control framework reinforces that principle: it highlights control activities such as proper authorizations/approvals and segregation of duties as fundamental to mitigating risks. In short, SOX compliance demands that moving any update into a production environment be subject to

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# Ensure the ScheduledTasks module is available
Import-Module ScheduledTasks

# Define the search string
$searchString = '\scheduled_reports\'

# Retrieve all scheduled tasks that have actions defined
$allTasks = Get-ScheduledTask | Where-Object { $_.Actions -ne $null }

# Initialize an array to store matching tasks

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from confluent_kafka import Consumer, TopicPartition

# Consumer configuration
conf = {
    'bootstrap.servers': 'your_broker',
    'group.id': 'your_group',
    'enable.auto.commit': False,
    'auto.offset.reset': 'none',
}
consumer = Consumer(conf)

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import networkx as nx
from ortools.sat.python import cp_model
from typing import List, Optional
import matplotlib.pyplot as plt


def generate_random_directed_graph(
        num_nodes: int,
        edge_prob: float,
        seed: int | None = None

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-- Assuming table t has one row with lower_bound and upper_bound
WITH Numbers AS (
    -- Anchor member: start with the lower bound
    SELECT lower_bound AS number
    FROM t
    UNION ALL
    -- Recursive member: increment the number by 1 each time
    SELECT number + 1
    FROM Numbers
    WHERE number + 1 <= (SELECT upper_bound FROM t)

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DECLARE @SchemaName NVARCHAR(128) = 'YourSchemaName';
DECLARE @SQL NVARCHAR(MAX) = '';

-- Select all columns that have a NOT NULL constraint
SELECT @SQL = @SQL + 'ALTER TABLE [' + s.name + '].[' + t.name + '] ALTER COLUMN [' + c.name + '] ' 
    + TYPE_NAME(c.system_type_id) 
    + CASE 
        WHEN c.max_length = -1 THEN '(MAX)' 
        WHEN c.system_type_id IN (231, 167) THEN '(' + CAST(c.max_length AS VARCHAR(10)) + ')'
        WHEN c.system_type_id IN (106, 108) THEN '(' + CAST(c.precision AS VARCHAR(10)) + ',' + CAST(c.scale AS VARCHAR(10)) + ')' -- Handle DECIMAL/NUMERIC

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def prorate_proportionally(df: pd.DataFrame, group: pd.DataFrame):
    # Get the total tonnage values from the first row
    total_tonnage_moved_contract = group.iloc[0]["tonnage_moved_contract"]
    total_tonnage_moved_spot = group.iloc[0]["tonnage_moved_spot"]
    total_tonnage_moved_unknown = group.iloc[0]["tonnage_moved_unknown"]

    # Calculate the total commitment
    total_commitment = group["tonnage_committed"].sum()

    # Compute proration factors for each row

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import msal
import httpx
from dynaconf import Dynaconf

# Load configuration
config = Dynaconf(
    settings_files=['settings.toml', '.secrets.toml'],
    environments=True,
    load_dotenv=True,
)

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import json
import pyodbc
import re
from datetime import date, datetime
from typing import Any, Dict, Optional, List

def load_json(file_path: str) -> Any:
    with open(file_path, 'r') as file:
        return json.load(file)

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import subprocess
import json

def run_powershell_command(command):
    completed_process = subprocess.run(
        ["powershell", "-Command", command],
        capture_output=True,
        text=True
    )
    if completed_process.returncode != 0: