MCP Audit Logging: Tracing AI Agent Actions for Compliance

Why Audit Logging Matters for MCP Systems

The autonomous nature of AI agents introduces unprecedented accountability challenges. Unlike traditional software where every action can be traced to a specific user command, AI agents make independent decisions based on context, instructions, and available tools. When an agent accesses sensitive customer data, modifies a database, or executes a financial transaction, organizations must be able to answer critical questions: Why did the agent take this action? What data informed the decision? Was the action authorized? Without comprehensive audit logs, these questions become impossible to answer.

Regulatory frameworks across industries mandate detailed logging of automated systems that handle sensitive data or make consequential decisions. Healthcare organizations subject to HIPAA must track every access to protected health information, including accesses made by AI agents. Financial institutions under SOX, PCI-DSS, or GDPR face similar requirements for tracking data access and system changes. MCP systems that enable agents to interact with regulated data or systems must produce audit trails that demonstrate compliance with these frameworks. The penalties for inadequate logging can be severe, ranging from failed audits to substantial fines and legal liability.

Beyond compliance, audit logs serve critical operational purposes. When an AI agent produces unexpected results or makes questionable decisions, audit logs provide the forensic data needed to understand what happened. They enable debugging of complex multi-agent workflows, help identify performance bottlenecks, and reveal patterns in agent behavior that might indicate security issues or system misconfigurations. In production environments, audit logs become the primary tool for incident response, allowing teams to quickly trace the sequence of events leading to a problem.

Security monitoring represents another crucial dimension of audit logging. AI agents with access to powerful tools can become vectors for attack if compromised or manipulated through prompt injection or other exploits. Comprehensive audit logs enable security teams to detect anomalous agent behavior, identify potential breaches, and respond to security incidents. They provide the evidence needed to determine whether an agent acted within its authorized scope or exceeded its permissions. Without this visibility, organizations operate blind to potential security threats within their AI systems.

The distributed nature of MCP architectures amplifies these challenges. A single user request might trigger a cascade of agent actions across multiple MCP servers, each invoking different tools and accessing different data sources. Tracking this distributed execution requires sophisticated logging infrastructure that can correlate events across systems while maintaining performance and reliability. The complexity of these systems makes audit logging not just important but essential for maintaining operational control and understanding system behavior.

Compliance Framework Requirements for AI Agent Logging

Different regulatory frameworks impose specific requirements on audit logging systems, and MCP implementations must be designed to satisfy the most stringent applicable standards. Understanding these requirements upfront ensures that logging infrastructure can meet compliance needs without requiring costly retrofitting later. The specific requirements vary by industry and jurisdiction, but several common themes emerge across frameworks.

HIPAA (Health Insurance Portability and Accountability Act) requires covered entities to maintain audit logs of all access to protected health information (PHI). For MCP systems handling healthcare data, this means logging every instance where an AI agent reads, writes, or transmits PHI. The logs must include the identity of the agent or system making the access, the specific data accessed, the timestamp, and the purpose of the access. HIPAA also requires that audit logs be retained for at least six years and protected against tampering or unauthorized access. MCP systems in healthcare must ensure that agent actions are logged with sufficient detail to demonstrate compliance during audits.

GDPR (General Data Protection Regulation) imposes requirements around processing of personal data, including automated decision-making. When AI agents process personal data of EU residents, organizations must be able to demonstrate lawful basis for processing and provide transparency about how decisions are made. Audit logs must capture what personal data was accessed, how it was used in decision-making, and the logic behind automated decisions. GDPR also grants individuals the right to explanation for automated decisions affecting them, making detailed audit logs essential for responding to such requests. The regulation requires that logs themselves be protected as they contain personal data.

SOX (Sarbanes-Oxley Act) requires public companies to maintain internal controls over financial reporting, including audit trails for systems that process financial data. MCP systems that enable agents to access financial records, execute transactions, or generate reports must produce comprehensive audit logs demonstrating proper controls. These logs must show segregation of duties, proper authorization for actions, and an unbroken chain of custody for financial data. SOX also requires that audit logs be retained for at least seven years and protected against alteration.

PCI-DSS (Payment Card Industry Data Security Standard) applies to systems that process, store, or transmit payment card data. The standard requires detailed logging of all access to cardholder data and regular review of logs for suspicious activity. For MCP systems handling payment data, this means logging every agent action involving card data, including the specific data elements accessed, the tools used, and the outcome of operations. PCI-DSS also mandates that logs be retained for at least one year, with three months immediately available for analysis, and that log data be protected against unauthorized modification.

What to Log: Actions, Tools, and Data Access

Determining what to log in MCP systems requires balancing completeness with performance and storage constraints. Logging too little leaves gaps in audit trails and compliance coverage; logging too much creates performance bottlenecks, storage challenges, and noise that obscures important events. A well-designed logging strategy captures the essential information needed for compliance, security, and operations while remaining sustainable at scale.

Agent Actions and Decisions

Every significant action taken by an AI agent should generate an audit log entry. This includes the initial request or trigger that activated the agent, the agent’s interpretation of the request, and each decision point in the agent’s execution. For example, when an agent decides to invoke a particular tool, the log should capture why that tool was selected, what parameters were provided, and what alternatives were considered. This decision-level logging is crucial for understanding agent behavior and explaining automated decisions to users or auditors.

The log entry for an agent action should include the agent identifier, the user or system context under which the agent operates, the timestamp, and a description of the action. It should also capture the agent’s reasoning or the prompt that led to the action, though care must be taken to sanitize any sensitive data from prompts before logging. Including the agent’s confidence level or uncertainty about decisions can provide valuable context for later analysis.

Tool Invocations and Parameters

When an MCP agent invokes a tool, comprehensive logging of the invocation is essential. The log should record the tool name, the specific operation or method called, and all parameters passed to the tool. However, parameter logging requires careful consideration of data sensitivity. While it’s important to log that an agent queried a database with a specific customer ID, logging the actual customer data returned would be inappropriate and potentially violate privacy regulations.

A practical approach is to log parameter metadata rather than values for sensitive fields. For example, instead of logging {"customer_id": "12345", "ssn": "123-45-6789"}, log {"customer_id": "<redacted>", "ssn": "<redacted>", "param_count": 2}. This preserves the audit trail while protecting sensitive data. For non-sensitive parameters, full values can be logged to aid in debugging and analysis.

Tool invocation logs should also capture the outcome: whether the tool call succeeded, any errors encountered, and summary information about results. For example, if an agent queries a database, log the number of records returned rather than the records themselves. If a tool call fails, log the error type, message, and any relevant diagnostic information.

Data Access Patterns

Tracking what data an agent accesses is critical for compliance and security. Each data access should generate a log entry identifying the data source, the specific data elements accessed, and the purpose of the access. For structured data like databases, this means logging table names, column names, and query predicates. For unstructured data like files or documents, log file paths, document identifiers, and access types (read, write, delete).

Data access logs should distinguish between different types of access. Reading data for display to a user differs from reading data to make an automated decision, which differs from modifying data. Each access type may have different compliance implications and should be logged accordingly. Additionally, track the scope of access: did the agent access a single record, a filtered set of records, or perform a full table scan?

Authentication and Authorization Events

Every authentication attempt and authorization decision should be logged. This includes successful and failed authentication attempts, token generation and validation, and permission checks. When an agent attempts to perform an action, log whether the agent had the necessary permissions and whether the action was allowed or denied. Failed authorization attempts are particularly important for security monitoring, as they may indicate attempted privilege escalation or system compromise.

Authorization logs should capture the identity making the request, the resource being accessed, the action being attempted, and the decision (allow/deny). Include the authorization policy or rule that governed the decision, as this helps in auditing policy effectiveness and debugging authorization issues.

System Events and State Changes

Beyond agent actions, log significant system events such as MCP server startup and shutdown, configuration changes, and connection events. When an agent connects to an MCP server or a tool provider becomes available or unavailable, these events should be logged. State changes in the system, such as agents being enabled or disabled, permission changes, or policy updates, are also important audit events.

Contextual Information

Every log entry should include rich contextual information that enables correlation and analysis. This includes:

• Request ID: A unique identifier for the original user request that triggered the agent activity
• Session ID: An identifier for the user session or conversation
• Agent ID: The specific agent instance performing the action
• User Context: Information about the user on whose behalf the agent is acting
• Trace ID: A distributed tracing identifier for correlating events across systems
• Environment: Information about the deployment environment (production, staging, etc.)
• Version: Agent version, MCP protocol version, and tool versions

This contextual information transforms individual log entries into a coherent narrative of system behavior, enabling powerful analysis and troubleshooting capabilities.

Distributed Tracing Across Multi-Agent Systems

MCP architectures often involve multiple agents collaborating to fulfill complex requests, with each agent potentially running on different servers and invoking different tools. A single user query might trigger a cascade of agent actions: one agent interprets the request, another retrieves data, a third performs analysis, and a fourth formats the response. Tracing this distributed execution requires sophisticated techniques that go beyond simple logging.

Distributed tracing provides the infrastructure to follow a request through its entire lifecycle across multiple services and agents. The core concept involves propagating a unique trace identifier through every component involved in handling a request. Each agent and tool that participates in the request adds its own span to the trace, creating a hierarchical view of the execution. This enables operators to see the complete flow of a request, identify bottlenecks, and understand the relationships between different agent actions.

Implementing Trace Propagation

Trace propagation in MCP systems requires careful coordination between agents, servers, and tools. When a user initiates a request, the system generates a unique trace ID and a root span ID. As the request flows through the system, each component creates child spans that reference the parent span, building a tree structure that represents the execution hierarchy.

The MCP protocol should include trace context in all messages between clients and servers. When an agent invokes a tool, it should pass the trace context as part of the invocation, allowing the tool to create its own span within the trace. This requires that tools support trace context propagation, either through explicit parameters or through standardized headers. Common standards like W3C Trace Context provide interoperable formats for trace propagation that work across different systems and vendors.

Each span in the trace should capture timing information (start time, duration), the operation being performed, and relevant metadata. For agent spans, this includes the agent’s reasoning, the tools it considered, and the decisions it made. For tool invocation spans, this includes parameters, results, and any errors. The combination of spans creates a complete picture of how a request was processed.

Tetrate Agent Router’s OpenInference integration addresses these distributed tracing challenges for MCP systems. OpenInference provides a standardized trace format designed specifically for AI workloads, capturing agent reasoning, tool invocations, and data access patterns in a unified schema. This enables correlation across agent boundaries while maintaining the semantic context needed for compliance analysis and debugging.

Correlation Across Agent Boundaries

When multiple agents collaborate on a request, maintaining correlation becomes challenging. An agent might delegate part of its work to another agent, or multiple agents might work in parallel on different aspects of a problem. The tracing system must capture these relationships while maintaining clarity about the execution flow.

One effective approach is to use parent-child relationships between spans to represent delegation, and sibling relationships to represent parallel execution. When Agent A delegates to Agent B, Agent B’s span becomes a child of Agent A’s span. When Agent A triggers multiple agents to work in parallel, each parallel agent’s span becomes a child of Agent A’s span, with the spans marked as concurrent.

The tracing system should also capture the communication patterns between agents. If agents communicate through message queues, API calls, or shared state, these interactions should be represented in the trace. This reveals not just what each agent did, but how agents coordinated and shared information.

Handling Asynchronous and Long-Running Operations

MCP agents often perform asynchronous operations or long-running tasks that don’t complete within a single request-response cycle. An agent might initiate a background job, schedule a future action, or wait for an external event. Tracing these operations requires special handling to maintain the connection between the initiating request and the eventual completion.

For asynchronous operations, create a span when the operation is initiated and update it when the operation completes. The span should remain open during the operation’s execution, even if this spans minutes or hours. This requires a tracing backend that can handle long-lived spans and efficiently query traces with extended durations.

For scheduled or delayed operations, create a new trace that references the original trace as its parent. This maintains the causal relationship while allowing the delayed operation to be traced independently. Include metadata that links back to the original request, enabling operators to understand why a scheduled operation was created.

Sampling and Performance Considerations

Tracing every request in a high-volume MCP system can create significant overhead and generate massive amounts of trace data. Sampling strategies help balance visibility with performance by selectively tracing a subset of requests. However, sampling must be done carefully to ensure important requests aren’t missed.

Head-based sampling makes the sampling decision at the start of a request based on the trace ID. This is simple and efficient but may miss interesting traces that only become important later. Tail-based sampling makes the decision after the request completes, allowing the system to sample based on characteristics like duration, errors, or specific operations performed. This captures more interesting traces but requires temporarily storing all trace data until the sampling decision is made.

For compliance-critical operations, consider always-on tracing regardless of sampling rates. Operations involving sensitive data, privileged actions, or regulated processes should always be traced to ensure complete audit trails. Use trace attributes to mark these critical operations and exempt them from sampling.

Visualizing and Analyzing Distributed Traces

The value of distributed tracing comes from the ability to visualize and analyze execution flows. Trace visualization tools display the hierarchical structure of spans, showing timing relationships, dependencies, and the critical path through the system. This makes it easy to identify slow operations, understand agent collaboration patterns, and diagnose issues.

Analysis of trace data can reveal patterns in agent behavior, identify common execution paths, and detect anomalies. For example, analyzing traces might show that certain agent combinations consistently produce better results, or that specific tool invocations are bottlenecks. This insight enables optimization of agent configurations and tool implementations.

Trace data should be integrated with other observability signals like logs and metrics. Correlating traces with log entries provides detailed context for specific operations, while correlating with metrics shows how individual requests relate to system-wide performance. This unified observability approach provides comprehensive visibility into MCP system behavior.

Storage, Retention, and Security for Audit Trails

Audit logs are only valuable if they remain accessible, trustworthy, and secure throughout their required retention period. The storage infrastructure for MCP audit logs must handle high write volumes, support efficient querying, ensure data integrity, and meet compliance requirements for retention and security. Designing this infrastructure requires careful consideration of storage technologies, retention policies, and security controls.

Storage Architecture for High-Volume Logging

MCP systems can generate enormous volumes of audit data, particularly in production environments with many active agents and high request rates. A single agent action might generate dozens of log entries as it invokes tools, accesses data, and makes decisions. Multiply this by hundreds or thousands of concurrent agents, and the logging system must handle millions of log entries per hour.

The storage architecture must be designed for write-heavy workloads while still supporting efficient queries. Time-series databases are well-suited for audit logs because they optimize for time-ordered writes and time-range queries. These databases typically compress data effectively and support retention policies that automatically age out old data. However, they may have limitations on complex queries or full-text search.

A common pattern is to use a hot-warm-cold storage tier approach. Recent logs (hot tier) are stored in fast, queryable storage optimized for operational use. Older logs (warm tier) are moved to more cost-effective storage that still supports queries but with higher latency. Very old logs (cold tier) are archived to object storage for long-term retention, with queries requiring rehydration of the data. This tiered approach balances performance, cost, and retention requirements.

Ensuring Log Integrity and Immutability

Audit logs must be tamper-proof to have value for compliance and security. If logs can be modified or deleted, they cannot be trusted as evidence of system behavior. Implementing immutability requires both technical controls and operational procedures.

Write-once storage systems provide immutability at the storage layer. Once a log entry is written, it cannot be modified or deleted until its retention period expires. This can be implemented using storage systems with WORM (Write Once Read Many) capabilities, or through application-level controls that prevent updates and deletes.

Cryptographic techniques can provide additional integrity guarantees. Each log entry can include a hash of its contents, and entries can be chained together where each entry includes the hash of the previous entry, similar to blockchain. This creates a tamper-evident log where any modification breaks the chain and is immediately detectable. Digital signatures can provide non-repudiation, proving that logs were generated by legitimate system components.

Access controls must prevent unauthorized modification of logs. Even system administrators should not be able to alter audit logs. Implement separation of duties where the logging system is managed independently from the application systems being logged. Use append-only APIs that don’t expose update or delete operations, and audit all access to the logging infrastructure itself.

Retention Policies and Lifecycle Management

Different types of audit data may have different retention requirements based on regulatory frameworks and operational needs. Compliance-critical logs might need to be retained for seven years or more, while operational logs might only need to be kept for months. Implementing effective retention policies requires classifying logs by type and applying appropriate retention periods.

Retention policies should be automated to ensure consistent application. Configure the storage system to automatically delete or archive logs when they reach the end of their retention period. However, implement safeguards to prevent premature deletion: require multiple approvals for retention policy changes, and maintain audit logs of retention policy modifications.

Consider legal hold requirements in retention policies. When litigation or investigations are pending, relevant logs must be preserved beyond their normal retention period. Implement mechanisms to place legal holds on specific logs or time ranges, preventing their deletion until the hold is released.

Encryption and Access Control

Audit logs often contain sensitive information and must be protected both at rest and in transit. Encrypt logs using strong encryption algorithms, with encryption keys managed through a proper key management system. Use separate encryption keys for different log types or sensitivity levels, allowing for granular control over access.

Implement strict access controls on audit logs. Access should be granted on a need-to-know basis, with different roles having access to different log types. For example, security analysts might have access to security-related logs, while compliance officers have access to compliance logs. All access to audit logs should itself be logged, creating an audit trail of audit trail access.

For highly sensitive logs, consider implementing break-glass procedures that require special authorization and generate alerts when accessed. This ensures that sensitive logs are only accessed when truly necessary and that such access is immediately visible to security teams.

Backup and Disaster Recovery

Audit logs are critical business records that must be protected against data loss. Implement regular backups of audit logs, with backups stored in geographically separate locations. Test backup restoration procedures regularly to ensure that logs can be recovered if needed.

Disaster recovery plans should include audit logs. In the event of a system failure or disaster, the ability to restore audit logs is essential for maintaining compliance and understanding what happened before the incident. Recovery time objectives (RTOs) and recovery point objectives (RPOs) for audit logs should be defined based on compliance requirements.

Compliance with Data Privacy Regulations

While audit logs are necessary for compliance, they must themselves comply with data privacy regulations. Logs should not contain unnecessary personal data, and any personal data in logs must be protected appropriately. Implement data minimization by logging only what’s necessary for audit purposes.

Consider the right to erasure under regulations like GDPR. While audit logs generally have legitimate grounds for retention that override erasure requests, this should be documented and justified. Implement procedures for handling erasure requests that balance privacy rights with audit requirements.

When logs must be shared with auditors or regulators, implement secure sharing mechanisms that protect log confidentiality. Use secure file transfer protocols, encrypt logs before sharing, and maintain audit trails of who accessed shared logs.

Production Operations: Monitoring and Compliance Reporting

Once audit logging infrastructure is in place, organizations must operationalize it through effective monitoring and reporting. Production MCP systems require continuous oversight to detect compliance violations, identify security incidents, and generate the reports needed for regulatory audits. This operational layer transforms raw audit logs into actionable intelligence and compliance evidence.

Alerting on Compliance-Relevant Events

Real-time alerting enables organizations to respond quickly to compliance violations and security incidents before they escalate. Effective alerting requires careful configuration to balance sensitivity with practicality—too many alerts lead to alert fatigue, while too few alerts miss critical events.

Threshold Configuration for Audit Anomalies: Establish baselines for normal system behavior and configure alerts for deviations that might indicate compliance issues. For example, alert when an agent accesses an unusually high volume of sensitive records, when failed authentication attempts exceed normal patterns, or when data access occurs outside expected time windows. Use statistical methods to identify anomalies rather than fixed thresholds, allowing the system to adapt to changing usage patterns while still catching outliers.

Implement rate-based alerts that trigger when specific events occur too frequently within a time window. For instance, alert if more than five failed authorization attempts occur within a minute, or if an agent makes more than 100 data access requests in an hour. These rate-based alerts can catch both technical issues and potential security incidents.

Real-Time Alerts for Unauthorized Access Attempts: Configure immediate alerts for high-severity events that represent clear compliance violations or security threats. These include attempts to access data without proper authorization, modifications to audit logs, access to highly sensitive data by unauthorized agents, or execution of prohibited operations. These alerts should trigger immediately and notify security teams through multiple channels to ensure rapid response.

Implement context-aware alerting that considers the full context of an action. An agent accessing financial data might be normal during business hours but suspicious at 3 AM. An agent reading customer records might be expected for a customer service agent but unusual for a marketing agent. Context-aware alerts reduce false positives while catching genuine threats.

Escalation Workflows for Compliance Violations: Define clear escalation paths for different types of compliance events. Minor violations might generate tickets for review during business hours, while critical violations trigger immediate pages to on-call security staff. Implement automated escalation where alerts that aren’t acknowledged within defined timeframes automatically escalate to higher-level responders.

Integrate alerting with incident response workflows. When a compliance violation is detected, automatically create an incident ticket, gather relevant context from audit logs, and route it to the appropriate team. This ensures that compliance events are tracked through resolution and that all required documentation is maintained.

Automated Compliance Reporting

Regulatory audits and internal compliance reviews require comprehensive reports demonstrating that systems meet compliance requirements. Automating report generation ensures consistency, reduces manual effort, and makes compliance evidence readily available when needed.

Scheduled Report Generation: Implement automated generation of compliance reports on regular schedules. Daily reports might summarize access to sensitive data, weekly reports might analyze authentication and authorization patterns, and monthly reports might provide comprehensive compliance metrics. Schedule reports to run during off-peak hours to minimize impact on production systems.

Design reports to directly address specific compliance requirements. For HIPAA compliance, generate reports showing all PHI access with user identities and purposes. For SOX compliance, generate reports demonstrating segregation of duties and proper authorization for financial transactions. For PCI-DSS compliance, generate reports on all cardholder data access and security event monitoring.

Implement report templates that can be customized for different audiences. Executive reports might focus on high-level compliance metrics and trends, while detailed technical reports provide the granular information needed by auditors. Ensure reports include all required elements such as report period, data sources, methodology, and any limitations or caveats.

Dashboard Creation for Compliance Teams: Build real-time dashboards that give compliance teams continuous visibility into system compliance status. Dashboards should display key compliance metrics, recent alerts, trends over time, and current compliance posture. Use visualization techniques like heat maps, trend lines, and status indicators to make compliance status immediately apparent.

Create role-specific dashboards tailored to different compliance functions. Security teams need dashboards focused on access control and security events. Privacy teams need dashboards showing personal data processing activities. Audit teams need dashboards that track audit log completeness and integrity. Each dashboard should present information relevant to its audience without overwhelming them with unnecessary detail.

Implement drill-down capabilities that allow users to investigate compliance metrics in detail. A dashboard might show a high-level metric like “unauthorized access attempts this week,” with the ability to click through to see specific incidents, affected resources, and full audit trails. This enables compliance teams to quickly investigate issues without requiring deep technical knowledge of the logging system.

Audit Preparation Automation: When preparing for regulatory audits, organizations must gather extensive evidence from audit logs. Automate this process by creating audit evidence packages that compile all relevant logs, reports, and documentation for specific audit periods. These packages should be organized according to the specific requirements of the regulatory framework being audited.

Implement continuous compliance validation that regularly checks whether logging infrastructure meets all compliance requirements. Automated tests should verify that all required events are being logged, logs are being retained for required periods, log integrity controls are functioning, and access controls are properly configured. Any gaps or issues should generate alerts so they can be addressed before an audit.

Create audit trails of the compliance reporting system itself. When reports are generated, log who requested them, what data was included, and who accessed them. When dashboards are viewed, log the user and timestamp. This meta-audit trail demonstrates that compliance monitoring is itself being conducted properly and provides evidence of ongoing compliance oversight.

Integration with Compliance Management Systems

Integrate MCP audit logging with broader compliance management platforms to provide a unified view of organizational compliance. Export compliance metrics and reports to GRC (Governance, Risk, and Compliance) systems, feed security events to SIEM (Security Information and Event Management) platforms, and provide audit evidence to compliance documentation systems. This integration ensures that MCP compliance is part of the organization’s overall compliance program rather than a siloed effort.

Maintain documentation of all monitoring and reporting configurations, including alert thresholds, report definitions, and dashboard designs. This documentation should be version-controlled and reviewed regularly to ensure it remains aligned with current compliance requirements. When regulations change or new compliance requirements emerge, update monitoring and reporting configurations accordingly and document the changes.

Common Compliance Requirements

Across regulatory frameworks, several common requirements emerge that should guide MCP audit logging design:

Identity and Authentication: Logs must clearly identify which agent or system performed each action, including authentication context and authorization scope. This includes tracking both the AI agent identity and any user context under which the agent operates.

Timestamp and Sequencing: Every logged event must include precise timestamps, typically with millisecond or microsecond precision. In distributed systems, synchronized time sources are essential to maintain accurate sequencing of events across multiple servers.

Data Access Tracking: Logs must record what data was accessed, including specific fields or records. For sensitive data, this might include logging data classifications, sensitivity levels, or specific identifiers without logging the actual data content.

Action and Outcome: Each log entry should capture what action was attempted, whether it succeeded or failed, and any relevant error information. This enables both compliance verification and operational troubleshooting.

Immutability and Integrity: Audit logs must be protected against tampering. This typically requires write-once storage, cryptographic signatures, or blockchain-like chaining to ensure logs cannot be altered after creation.

Retention and Accessibility: Logs must be retained for specified periods and remain accessible for audit purposes. This requires planning for long-term storage, archival strategies, and efficient retrieval mechanisms.