Data Integrity Audit: Your Complete Guide to FDA Compliance

A data integrity audit is a systematic examination of data lifecycle processes, electronic systems, and quality controls to ensure pharmaceutical records meet ALCOA+ principles (Attributable, Legible, Contemporaneous, Original, Accurate, plus Complete, Consistent, Enduring, and Available). These audits verify that critical GMP data remains trustworthy from creation through retention and eventual destruction.

If you're a QA manager or data integrity officer, you already know the stakes. A single data integrity finding during an FDA inspection can trigger warning letters, import alerts, or consent decrees that halt production for months. The 2018 FDA guidance on Data Integrity and Compliance with CGMP made one thing clear: regulators now scrutinize electronic records with the same intensity they apply to manufacturing processes.

Yet most pharmaceutical companies approach data integrity audits reactively, scrambling weeks before an inspection rather than building robust, audit-ready systems. This guide changes that.

In this guide, you'll learn:

• How to prepare for pharmaceutical data integrity audits using the ALCOA+ framework
• What FDA inspectors look for during data integrity inspections and how to address gaps before they find them
• Proven strategies for implementing data integrity compliance programs that withstand regulatory scrutiny
• Common data integrity violations found in warning letters and how to prevent them in your systems

What Is a Data Integrity Audit? [Definition]

A data integrity audit is a comprehensive evaluation of an organization's data lifecycle management, examining how pharmaceutical data is created, modified, stored, transferred, and archived across paper-based and electronic systems. Unlike routine quality audits that focus on specific processes, data integrity audits assess the fundamental trustworthiness of the data supporting regulatory submissions, batch release decisions, and GMP compliance.

Key characteristics of data integrity audits:

• Scope spans entire data lifecycle - From initial data capture through retention and archival, covering all systems that generate or store GMP-critical data
• Focus on ALCOA+ principles - Systematic verification that records are Attributable, Legible, Contemporaneous, Original, Accurate, Complete, Consistent, Enduring, and Available
• System-level and procedural assessment - Examines both technical controls (audit trails, user access, validation) and human processes (training, oversight, CAPA)
• Risk-based methodology - Prioritizes high-impact systems like LIMS, HPLC, stability chambers, and manufacturing execution systems over low-risk equipment

The regulatory imperative for data integrity audits:

The FDA, EMA, MHRA, and other global regulators now explicitly require companies to demonstrate data integrity through documented governance programs. The 2018 FDA guidance states: "You should have systems and procedures in place to detect inappropriate data manipulation or deletion." This means:

• Proactive auditing is mandatory - Waiting for an inspection to discover data integrity gaps is non-compliant
• Documentation of governance is required - You must demonstrate systematic oversight through audit programs, metrics, and CAPA
• Electronic systems require heightened scrutiny - 21 CFR Part 11 compliance alone is insufficient; you must also address data integrity principles

Data integrity audits differ fundamentally from general quality audits in three ways:

Understanding ALCOA Data Integrity Principles

The ALCOA data integrity framework originated in FDA's 2003 Part 11 guidance but has evolved into the ALCOA+ standard now referenced across global regulatory guidance. These nine principles define what makes pharmaceutical data trustworthy and form the foundation of every data integrity audit.

The Original ALCOA Framework

Attributable - Every data point must clearly identify who created it and when. For electronic records, this requires:

• Unique user credentials (no shared logins)
• Timestamp accuracy synchronized to validated time sources
• Indelible linkage between data and creator identity
• Metadata preservation showing operator, instrument, and datetime

Legible - Data must remain readable throughout its lifecycle. This means:

• Electronic data viewable in human-readable format without specialized software knowledge
• Printouts or electronic displays that preserve all critical information
• Archived data retrievable and readable after system migrations
• No degradation of scanned documents or electronic signatures over time

Contemporaneous - Data must be recorded at the time the activity occurs, not backfilled. Key requirements:

• Real-time or near-real-time data capture by systems
• Prohibition of post-dated entries in batch records
• Timestamped audit trails showing actual execution time vs. documentation time
• Investigation of any significant lag between event and documentation

Original - The first capture of data, or a true copy that preserves all metadata. This principle requires:

• Retention of raw data files from instruments (not just processed results)
• Preservation of metadata and audit trails alongside data
• True copies that are complete, exact duplicates with hash verification
• No substitution of transcribed data for original electronic records

Accurate - Data must be correct, complete, and free from errors. Accuracy verification includes:

• Instrument calibration and qualification
• Data review and approval by qualified personnel
• Error detection through automated checks and manual review
• Investigation and documentation of all data anomalies

The ALCOA+ Extensions (Complete, Consistent, Enduring, Available)

Modern regulatory guidance expands ALCOA to ALCOA+ with four additional principles:

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Critical Distinction: ALCOA focuses on data creation and initial capture, while the "+" extensions address data lifecycle management, archival, and long-term accessibility concerns that became critical as companies moved from paper to electronic systems.

Applying ALCOA+ to Different Data Types

Data integrity requirements scale with data criticality and risk. Here's how ALCOA+ principles apply across common pharmaceutical data types:

GMP-Critical Data (Highest Risk):

• Manufacturing batch records
• Laboratory test results for batch release
• Stability study data
• Validation protocols and reports
• Deviation and CAPA records

Requirement: Full ALCOA+ compliance with validated systems, comprehensive audit trails, and no unreviewed changes.

Supporting Data (Medium Risk):

• Environmental monitoring
• Equipment maintenance logs
• Training records
• Change control documentation

Requirement: ALCOA+ compliance with risk-appropriate controls; automated audit trails strongly preferred.

Non-GMP Data (Lower Risk):

• Meeting minutes
• General correspondence
• Preliminary research data
• Draft documents

Requirement: Basic attributability and accuracy; audit trail may be procedural rather than system-enforced.

FDA Data Integrity Inspection Checklist

When FDA investigators arrive for a data integrity inspection, they follow a systematic approach to assess your data governance program. Understanding their methodology helps you prepare effectively and identify gaps before regulators find them.

Pre-Inspection Document Requests

FDA's Pharmaceutical Inspectorate now routinely issues pre-inspection data requests 4-6 weeks before arrival.

System Inventories:

• Complete list of all computerized systems used in GMP operations
• Network diagrams showing data flow between systems
• Validation status of each system
• Current software versions and patch levels

Governance Documentation:

• Data integrity policy and procedures
• Data governance organizational chart
• Training curriculum on data integrity
• Audit program and schedule for data integrity reviews

Audit Trail and Metadata:

• Sample audit trail reports from critical systems
• Procedures for audit trail review
• Documentation of audit trail review completion
• CAPA records for audit trail findings

Access Controls:

• User access matrices for electronic systems
• Procedures for account provisioning and deprovisioning
• Evidence of periodic access reviews
• Shared account usage justifications (if any exist)

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Inspection Reality: Companies that cannot produce organized, readily accessible responses to pre-inspection requests immediately signal data integrity weaknesses. FDA interprets poor documentation retrieval as lack of governance oversight.

On-Site Inspection Focus Areas

FDA investigators spend 60-70% of data integrity inspection time in three locations: the quality control laboratory, data center/server room, and with electronic system administrators. Here's what they examine:

In the Laboratory:

• Observation of analysts performing tests with electronic instruments
• Request to see "rejected" or "invalidated" runs in HPLC/GC systems
• Comparison of handwritten logbooks against electronic system records
• Review of data file structures showing raw data vs. processed results
• Questioning analysts about procedures for handling failed tests

At the Data Center:

• Inspection of server access logs
• Review of backup and disaster recovery procedures
• Testing of data retrieval from archival systems
• Examination of user privilege settings in databases
• Verification of system administrator activity logging

With IT/QA Leadership:

• Demonstration of audit trail review process
• Explanation of system validation approach
• Discussion of risk assessments for data integrity
• Review of metrics tracking data integrity performance
• Analysis of CAPA effectiveness for prior findings

Common Inspection Triggers (What Gets You in Trouble)

FDA data integrity inspections escalate quickly when investigators find these red flags:

Pharmaceutical Data Integrity Audit Preparation

Successful data integrity audits require months of preparation, not weeks. Companies that consistently pass regulatory scrutiny build year-round programs addressing governance, systems, processes, and culture.

Building a Data Governance Framework

A robust data governance framework assigns clear accountability for data integrity across the organization and establishes systematic oversight mechanisms.

Organizational Structure:

Core Governance Documents Required:

1. Data Integrity Policy - Executive-level document establishing organizational commitment, defining ALCOA+ application, and assigning accountability
2. Data Lifecycle Procedures - Detailed SOPs for data creation, modification, archival, retrieval, and destruction across all system types
3. Risk Assessment Methodology - Framework for categorizing systems and data by criticality and determining appropriate controls
4. Audit Program - Schedule and scope for self-inspections, vendor audits, and system assessments
5. Training Curriculum - Role-based training on data integrity principles, procedures, and system-specific controls

Conducting Pre-Audit Risk Assessments

Before launching data integrity audits, complete a comprehensive risk assessment to prioritize audit focus and allocate resources effectively.

Step 1: Create a Complete System Inventory

Document every system that creates, modifies, or stores GMP data:

• Laboratory instruments (HPLC, GC, dissolution, spectrophotometers)
• Laboratory information management systems (LIMS)
• Manufacturing execution systems (MES)
• Document management and quality management systems
• Environmental monitoring and warehouse management systems
• Enterprise resource planning (ERP) modules handling GMP data

Step 2: Assess Each System's Data Criticality

Apply this scoring framework to determine criticality:

Total scores: 5-7 = Low Risk | 8-11 = Medium Risk | 12-15 = High Risk

Step 3: Evaluate Control Maturity

For each high and medium-risk system, assess current data integrity control maturity:

Control Maturity Assessment:

Developing Your Audit Program

Structure your data integrity audit program in three tiers:

Tier 1: Continuous Monitoring (Monthly)

• Automated audit trail reports from critical systems
• Access review reports showing new accounts, deletions, privilege changes
• System availability and backup success metrics
• Training completion rates by department
• Overdue investigation and CAPA aging reports

Tier 2: Focused System Audits (Quarterly)

• Deep dive into 2-3 high-risk systems per quarter
• Complete ALCOA+ assessment against current state
• Interview operators and review actual usage vs. procedures
• Test data retrieval and archive functionality
• Review validation status and gap analysis

Tier 3: Comprehensive Data Governance Audits (Annual)

• Assessment of governance committee effectiveness
• Policy and procedure review for currency and adequacy
• Cross-functional process mapping to identify gaps
• Vendor audit of critical hosted/cloud systems
• Benchmarking against industry standards and recent FDA guidance

Creating Inspection-Ready Documentation Packages

When FDA requests documentation, you should produce organized, complete packages within hours, not days. Prepare these standing packages:

Package 1: Data Governance Overview

• Data integrity policy (current, approved version)
• Organizational chart showing data governance roles
• Summary of annual audit plan and completion status
• Metrics dashboard (last 12 months of KPIs)
• CAPA summary for data integrity findings (last 24 months)

Package 2: System Summaries (One Per Critical System)

• System description and GMP scope
• Validation summary with current status
• User access matrix (current, with review date)
• Sample audit trail report (last 30 days)
• Evidence of monthly audit trail reviews (last 6 months)
• Training roster showing qualified users

Package 3: Procedure Index

• Master list of all data integrity SOPs with version and effective date
• Quick reference showing which SOP governs which system
• Training completion matrix by SOP and role

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Inspection Tip: Create a "Data Integrity Inspection Binder" (physical or electronic) that contains all three packages plus contact information for subject matter experts. Designate one person to manage inspector requests and route questions to appropriate personnel.

Data Integrity Compliance Strategies That Work

Passing a data integrity audit requires more than documentation. You need technical controls, process discipline, and cultural commitment working together systematically.

Technical Controls and System Configuration

Effective data integrity programs implement layered technical controls that make non-compliance difficult and detection certain.

Essential System Configuration Requirements:

Instrument Data Integrity Configuration:

Laboratory instruments present unique data integrity challenges. Here's how to configure common instrument types:

HPLC/GC Systems:

• Enable and lock audit trail functionality (cannot be disabled by operators)
• Configure automatic data export to LIMS or validated network storage
• Restrict reprocessing/reintegration to documented, justified circumstances with supervisor approval
• Retain all raw data files (chromatograms) with associated method files and metadata
• Prevent overwriting of original files when reprocessing

Dissolution/UV-Vis Spectrophotometers:

• Require unique user login before testing
• Archive all spectra and raw data, not just calculated results
• Implement sample identification that links to batch records
• Enable audit trail showing any post-acquisition processing

pH Meters, Balances, and Other Standalone Instruments:

• Use instruments with printer outputs that timestamp and identify operator
• Implement logbook procedures to capture all readings, including out-of-range values
• For manual transcription, require second-person verification
• Consider upgrading to instruments with electronic data capture and USB/network connectivity

Procedural Controls and SOPs

Technical controls fail without robust procedures governing their use. Essential procedural controls include:

SOP 1: Audit Trail Review

Define who reviews audit trails, how often, what to look for, and how to document findings.

Critical elements:

• Review frequency: Monthly minimum for GMP-critical systems, quarterly for supporting systems
• Reviewer qualifications: QA personnel with system knowledge
• Review scope: All creates, edits, deletes, access changes, configuration changes
• Flagging criteria: Changes without change control, off-hours access, repeated failed attempts
• Documentation: Completed checklist signed/dated by reviewer; exceptions investigated within 5 business days

SOP 2: Data Lifecycle Management

Establish procedures for each phase of the data lifecycle:

SOP 3: Handling Out-of-Specification (OOS) Results

OOS investigations are a primary area for data integrity violations. Procedures must require:

• Immediate notification of supervisor upon OOS result
• Retention of all testing records (passed and failed)
• Laboratory investigation before any retesting
• Scientific justification for result invalidation
• QA review and approval of investigation conclusions

SOP 4: User Access Management

Define processes for granting, modifying, and revoking system access:

• Standardized request and approval workflow
• Role-based access templates preventing privilege creep
• Onboarding and offboarding checklists ensuring timely provisioning
• Quarterly access recertification by department managers
• Immediate access removal for terminated employees

Training and Culture Building

Technical and procedural controls only work when people understand their importance and feel empowered to follow them.

Data Integrity Training Curriculum:

Building a Culture of Data Integrity:

Training alone doesn't change behavior. Effective data integrity culture requires:

1. Leadership Commitment - Executives publicly support data integrity, allocate resources for remediation, and recognize employees who identify issues
2. Speak-Up Environment - Employees can raise data integrity concerns without fear of retaliation; anonymous reporting mechanisms exist
3. Accountability - Performance evaluations include data integrity metrics; violations have consequences
4. Continuous Improvement - CAPA systems address root causes, not just symptoms; lessons learned are shared across sites
5. Transparency - Metrics are visible to all staff; trends are discussed in departmental meetings

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Cultural Indicator: If operators routinely ask "What's the right thing to do for data integrity?" rather than "What's the minimum to pass an audit?", your culture is maturing.

Common Data Integrity Violations and Prevention

Understanding actual FDA warning letter citations helps you identify and prevent the same violations in your operations.

Analysis of Recent FDA Warning Letters

Between 2020-2025, FDA issued over 200 warning letters citing data integrity deficiencies. Here are the most frequent violations and their frequencies:

Violation Deep Dive: Failed Tests Not Retained

The Problem:

Analysts perform multiple test runs, obtain an OOS result, repeat the test until obtaining a passing result, and document only the passing result in batch records. The failed tests are deleted or not documented.

Why It Happens:

• Analysts believe they're following "test until it passes" instruction
• Systems allow deletion without audit trail
• SOPs ambiguously permit "invalidation" without investigation
• No oversight of total number of tests vs. documented tests

Prevention Strategy:

• Configure instruments to retain all data files with sequential, non-deletable run numbers
• Implement LIMS that captures all sample results automatically
• Require laboratory investigation (per OOS SOP) before any retesting
• Reconcile sample inventory against number of documented tests
• Include "number of tests performed" in audit trail reviews

Detection Method:

• Compare file creation timestamps in instrument data folders against documented test dates
• Review instrument audit trails for deleted runs
• Analyze analyst behavior patterns (e.g., testing always passes on third attempt)
• Examine sample tracking to confirm all aliquots tested are documented

Violation Deep Dive: Inadequate Controls Over Computerized Systems

The Problem:

Electronic systems lack adequate access controls, change management, or validation, allowing unrestricted data manipulation without detection.

Why It Happens:

• Legacy systems implemented before current data integrity focus
• IT and QA working in silos without coordinated governance
• Vendor default configurations accepted without assessment
• Lack of resources for system upgrades or validation

Prevention Strategy:

Detection Method:

• Conduct gap assessment of all computerized systems against 21 CFR Part 11 and data integrity guidance
• Review system administrator access logs for unauthorized configuration changes
• Test user permissions to verify segregation of duties
• Request vendor audit trails to confirm no backdoor access

Violation Deep Dive: Data Manipulation or Falsification

The Problem:

Operators intentionally alter data to achieve desired results, delete unfavorable data, or backdate entries to appear compliant.

Why It Happens:

• Production pressure to release batches on schedule
• Inadequate training on scientific integrity
• Fear of consequences for legitimate OOS results
• Weak technical controls that allow manipulation
• Culture that prioritizes compliance appearance over data truth

Prevention Strategy:

Technical Barriers:

• Implement systems where data flows automatically from instruments to LIMS without manual transfer
• Use cryptographic hashing to detect any file alteration
• Configure time-stamping from validated, tamper-proof sources
• Enable read-only access to original data files after initial capture

Procedural Safeguards:

• Require second-person review for all critical data entries
• Implement statistical process control to identify improbable results
• Separate testing and data review responsibilities
• Create anonymous reporting mechanisms for data integrity concerns

Cultural Elements:

• Train extensively on consequences of falsification (regulatory and personal)
• Reward employees who identify and report data issues
• Investigate root causes of OOS without immediate blame
• Demonstrate leadership commitment to data integrity over convenience

Detection Method:

• Conduct unannounced observation of laboratory testing
• Analyze metadata (file creation/modification timestamps) for anomalies
• Compare digital signatures timestamps against actual work performed
• Interview staff about pressure to achieve specific results

Data Integrity Audit Execution

Once preparation is complete, executing the actual audit requires systematic methodology, thorough documentation, and objective assessment.

Audit Scope and Planning

Pre-Audit Activities:

1. Define Audit Scope

- Systems to be audited (select based on risk assessment)

- Time period for record review (typically 6-12 months)

- Departments and processes involved

- Specific ALCOA+ principles to emphasize

1. Assemble Audit Team

- Lead auditor with data integrity expertise

- Technical auditor with IT/system knowledge

- Quality auditor with regulatory compliance background

- Subject matter expert for specific system types if needed

1. Develop Audit Plan and Checklist

- Opening meeting agenda

- Daily schedule with specific activities and personnel

- Audit checklist organized by ALCOA+ principles

- Closing meeting agenda

- Target completion timeline

Sample Audit Checklist Structure:

Conducting the Audit

Opening Meeting:

• Introduce audit team and explain audit purpose
• Review audit scope, timeline, and logistics
• Establish point of contact for questions and document requests
• Set expectations for audit trail access and system demonstrations
• Schedule daily briefings and closing meeting

Document Review Phase:

Systematically examine these document types:

System Testing Phase:

Don't just review documentation. Actually test the systems:

Access Control Testing:

• Attempt to log in with invalid credentials (should fail)
• Log in with low-privilege account and attempt administrative action (should fail)
• Request list of all users and compare to authorized personnel list
• Identify any shared accounts or generic usernames

Audit Trail Testing:

• Create a test record, modify it, and delete it
• Review audit trail to verify all actions captured with user/timestamp
• Check if audit trail can be disabled (it shouldn't be possible)
• Verify audit trail is included in backups

Data Integrity Testing:

• Retrieve archived data from a specified date to verify availability
• Request raw data files and verify metadata preservation
• Check if original data files can be overwritten (should be prevented)
• Compare electronic records to printed/signed documents for consistency

Backup/Restore Testing:

• Request evidence of most recent backup success
• Ask when last restore test was performed
• If feasible, request demonstration of data retrieval from backup

Observation of Personnel

Watch people actually using systems in real-world conditions:

Laboratory Observations:

• Observe analyst login (individual account or shared?)
• Watch test execution and data recording
• Ask analyst to show where previous test results are stored
• Request demonstration of how they handle an OOS result
• Check if analyst can delete or overwrite data

Questions to Ask Operators:

• "Show me where all your test results are stored, including failed runs."
• "How do you handle it if a test result is out of specification?"
• "Can you show me the audit trail for the last test you performed?"
• "What happens if you make a mistake entering data?"
• "How do you know which version of the procedure to follow?"

Findings Documentation and Classification

Classify findings by severity:

Writing Effective Findings:

Good finding format:

Closing Meeting and Report

Closing Meeting Agenda:

1. Thank auditees for cooperation
2. Summarize audit scope and activities performed
3. Present findings by classification (Critical → Major → Minor → Observations)
4. Discuss preliminary root causes if identified
5. Outline expected response timeline
6. Set follow-up audit date if critical findings identified

Audit Report Contents:

• Executive summary (1-2 pages)
• Audit scope and methodology
• Systems and areas audited
• Summary of findings by category
• Detailed finding descriptions with evidence
• Positive observations (acknowledge good practices)
• Recommendations for improvement
• Required response timeline

Data Integrity CAPA and Continuous Improvement

Identifying findings through audits is only valuable if you implement effective corrective and preventive actions (CAPA) and prevent recurrence.

Root Cause Analysis for Data Integrity Findings

Superficial CAPA is a frequent FDA criticism. Effective root cause analysis for data integrity findings requires going beyond "lack of training" to identify systemic causes.

Apply the 5 Whys Technique:

Example for finding: "Shared user accounts found on three HPLC systems"

1. Why were shared accounts used? Analysts couldn't remember individual passwords.
2. Why couldn't they remember passwords? Complex password policy (16 characters, special symbols) was difficult to memorize.
3. Why was such a complex policy implemented? IT department applied enterprise IT policy to GMP systems without risk assessment.
4. Why didn't GMP leadership challenge this? No cross-functional data governance committee to resolve IT vs. GMP conflicts.
5. Why is there no governance committee? Data integrity governance structure was never formally established.

Root Cause: Lack of formal data governance structure to balance security requirements with GMP practicality, leading to password complexity that drove workaround behaviors.

Fishbone Diagram Categories for Data Integrity:

Effective CAPA Development

CAPA Characteristics That Work:

Tracking Data Integrity Metrics

Effective data integrity programs track both lagging indicators (findings, violations) and leading indicators (process health).

Recommended Data Integrity KPIs:

Dashboard Presentation:

Create executive dashboards showing:

• Trend charts for each KPI over last 12 months
• Red/yellow/green status against targets
• Top 3 systems with most data integrity findings
• Summary of open critical CAPAs with aging
• Training completion by department

Tools and Technology for Data Integrity Management

Modern data integrity programs leverage technology to automate monitoring, enforce controls, and simplify compliance.

Data Integrity Software Categories

Implementing Automated Audit Trail Review

Manual review of audit trails becomes impractical as system usage grows. Automation enables more comprehensive, risk-based review.

Automated Review Approach:

Step 1: Aggregate Audit Trail Data

• Extract audit trails from all critical systems to centralized repository
• Standardize data format (user, action, timestamp, object, old value, new value)
• Enrich with contextual data (user role, department, shift, change control linkage)

Step 2: Apply Risk-Based Rules

• Flag high-risk actions automatically (deletions, configuration changes, access modifications)
• Identify anomalies (off-hours access, unusual patterns, repeated failures)
• Detect policy violations (change without change control, access after termination)

Step 3: Present Prioritized Findings

• Dashboard showing critical alerts requiring immediate review
• Medium-risk findings for weekly review
• Low-risk log available for sampling
• Trend reports showing patterns over time

Step 4: Document Review and Disposition

• QA reviewer assesses flagged items
• Document justification for acceptable variances
• Initiate investigation for unexplained findings
• Archive review records with electronic signature

Example Risk Rules:

Cloud and Hosted System Considerations

Many pharmaceutical companies now use cloud-based or vendor-hosted systems (cloud LIMS, SaaS QMS, hosted EDC). Data integrity responsibility remains with the pharmaceutical company despite vendor management of infrastructure.

Key Data Integrity Questions for Cloud/SaaS Vendors:

1. Data Location and Sovereignty

- Where is data physically stored (country/region)?

- Can you guarantee data doesn't cross certain jurisdictions?

- How is data segregated from other customers?

1. Access Controls

- Does vendor personnel have access to customer data?

- Under what circumstances and with what approval?

- Is vendor access logged and auditable?

- Can vendor access be monitored in real-time?

1. Audit Trail and Logging

- Are audit trails customer-accessible and exportable?

- Do audit trails capture vendor administrative actions?

- How long are audit logs retained?

- Can audit trail functionality be disabled?

1. Backup and Business Continuity

- What is backup frequency and retention period?

- Can customer initiate restore or only vendor?

- What is recovery time objective (RTO) and recovery point objective (RPO)?

- Has disaster recovery been tested and documented?

1. Validation and Compliance

- Does vendor provide validation documentation packages?

- Is system developed under quality management system (ISO 9001, etc.)?

- Are SOC 2 Type II or ISO 27001 audits available?

- How are software updates validated before deployment?

Vendor Audit Approach:

For critical cloud/SaaS systems, conduct periodic vendor audits focusing on:

• Data center tour (if permitted) or video walkthrough
• Review of vendor's change control and software development lifecycle
• Assessment of vendor's data integrity policies and training
• Testing of data export/retrieval capabilities
• Review of vendor audit trails showing your data access
• Verification of backup/restore procedures

Key Takeaways

Next Steps

Data integrity audits are no longer optional regulatory exercises but fundamental requirements for pharmaceutical manufacturing authorization and market access. The companies that thrive in today's regulatory environment build data integrity into daily operations through robust governance, validated systems, risk-based monitoring, and a culture where data truthfulness is everyone's responsibility.

Organizations managing regulatory submissions benefit from automated validation tools that catch errors before gateway rejection. Assyro's AI-powered platform validates eCTD submissions against FDA, EMA, and Health Canada requirements, providing detailed error reports and remediation guidance before submission.

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