Automating Log Collection with MaaS360 Log ViewerAutomating log collection is a force multiplier for IT operations teams: it reduces manual work, speeds troubleshooting, and ensures consistent forensic records when devices misbehave. IBM MaaS360’s Log Viewer is a useful tool for inspecting device logs, but manually collecting logs from many endpoints is slow and error-prone. This article explains how to design and implement an automated log collection workflow using MaaS360 Log Viewer, including preparation, methods, best practices, and sample automation patterns.
Why automate log collection?
- Faster troubleshooting: Automated workflows gather the necessary logs immediately after an incident, reducing mean time to resolution (MTTR).
- Consistency: Scripts and policies ensure the same log types and formats are collected every time.
- Scale: Automation lets teams collect logs from hundreds or thousands of devices without manual steps.
- Auditability: Automated processes can timestamp, tag, and store logs in centralized locations for later analysis or compliance needs.
What the MaaS360 Log Viewer provides
MaaS360 Log Viewer lets administrators inspect logs from managed endpoints, including device diagnostics, app logs, and connection events. It supports collecting logs via the MaaS360 console and exporting them for offline analysis. For automation, use the MaaS360 APIs and built-in remote commands combined with the Log Viewer’s export capabilities to pull logs programmatically.
High-level architecture for automated collection
A typical automated log collection pipeline looks like:
- Detection — An alert or scheduled trigger identifies devices needing logs (SIEM alert, monitoring rule, or periodic sweep).
- Request — Automation issues a remote log collection command via MaaS360 APIs or console remote actions.
- Retrieval — Collected logs are exported from MaaS360 (API or SFTP/secure storage) or pulled from endpoints directly.
- Processing — Normalize, parse, and enrich logs (timestamps, device metadata, tags).
- Storage & Analysis — Store in a central repository (S3, Azure Blob, SIEM, or ELK stack) and run analysis/retention policies.
Prerequisites
- MaaS360 tenant with appropriate admin API access and permissions.
- API credentials: client ID and secret or API token with rights to issue remote commands and retrieve exports.
- Network/IT policies permitting outbound connections from devices if pulling logs directly.
- A logging backend (SIEM, ELK, cloud storage) and credentials for storing collected logs.
- Scripting environment (Python/PowerShell) or an automation/orchestration tool (Ansible, Jenkins, Microsoft Power Automate) to run workflows.
Available collection methods
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MaaS360 Remote Actions (recommended)
- Use MaaS360’s remote command features to trigger device-side log collection (e.g., collect debug logs).
- Pros: Centralized, supported, works even when users aren’t local to device.
- Cons: Dependent on MaaS360 agent capabilities and policy configuration.
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MaaS360 REST APIs
- The MaaS360 APIs can be used to request logs and download exported files programmatically. Many tenants expose endpoints for retrieving diagnostic bundles.
- Pros: Scriptable, suitable for large-scale automation.
- Cons: Requires secure credential handling and rate-limit handling.
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Endpoint-side agents or scripts
- Deploy scripts or a lightweight agent via MaaS360 that periodically uploads logs to a central endpoint or responds to remote triggers.
- Pros: Fine-grained control over what is collected.
- Cons: Requires deployment and maintenance of custom tooling.
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Hybrid
- Combine MaaS360 commands with agent-side upload to a central collector for large files or specialized logs.
Step-by-step automated workflow (example using APIs + Python)
This section outlines a concise Python-based pattern. Adjust for your environment, authentication model, and error handling.
- Authenticate to MaaS360 API (client credentials or token exchange).
- Query devices by criteria (OS, group, last seen) to build a target list.
- Issue a remote log collection command for each device (or batch) and record the command ID.
- Poll the command status until completed or failed.
- When logs are ready, download the export bundle.
- Extract, parse, and tag logs with device metadata.
- Upload processed logs to centralized storage (S3/SIEM).
- Clean up temporary files and record audit entries.
Notes:
- Use exponential backoff when polling.
- Parallelize steps for large fleets but respect API rate limits.
- Encrypt logs in transit and at rest.
Sample Python pseudocode
# Requires: requests, boto3 (if uploading to S3) # Pseudocode — adapt to your auth flow and API endpoints import requests, time, json from concurrent.futures import ThreadPoolExecutor MAAS360_BASE = "https://api.maas360.example" # tenant-specific CLIENT_ID = "xxx" CLIENT_SECRET = "yyy" S3_BUCKET = "my-log-archive" def get_token(): resp = requests.post(f"{MAAS360_BASE}/oauth/token", data={ "grant_type":"client_credentials", "client_id":CLIENT_ID, "client_secret":CLIENT_SECRET }) return resp.json()["access_token"] def list_devices(token, query_params): headers = {"Authorization": f"Bearer {token}"} r = requests.get(f"{MAAS360_BASE}/devices", headers=headers, params=query_params) return r.json()["devices"] def trigger_log_collection(token, device_id): headers = {"Authorization": f"Bearer {token}"} payload = {"action":"collect_logs", "options": {"type":"diagnostic"}} r = requests.post(f"{MAAS360_BASE}/devices/{device_id}/actions", headers=headers, json=payload) return r.json()["command_id"] def poll_status(token, device_id, command_id, timeout=300): headers = {"Authorization": f"Bearer {token}"} deadline = time.time()+timeout while time.time() < deadline: r = requests.get(f"{MAAS360_BASE}/devices/{device_id}/actions/{command_id}", headers=headers) status = r.json().get("status") if status in ("completed","failed"): return r.json() time.sleep(5) raise TimeoutError("Log collection timed out") def download_export(token, export_url, local_path): headers = {"Authorization": f"Bearer {token}"} r = requests.get(export_url, headers=headers, stream=True) with open(local_path, "wb") as f: for chunk in r.iter_content(1024*64): f.write(chunk) # Orchestrate token = get_token() devices = list_devices(token, {"os":"Android"}) with ThreadPoolExecutor(max_workers=10) as ex: for dev in devices: ex.submit(process_device, dev)
Parsing and enrichment
- Normalize timestamps to UTC and ISO 8601.
- Enrich with device attributes: user, group, OS version, last known IP.
- Tag logs with incident ID, alert rule, or ticket number.
- Extract structured fields (JSON parsing, regex) for SIEM ingestion.
Storage, retention, and compliance
- Store raw bundles and parsed logs separately: raw for forensic integrity, parsed for analytics.
- Apply retention rules matching your compliance requirements (e.g., 90 days for operational logs, longer for audit).
- Encrypt stored logs (AES-256) and manage keys via a KMS.
- Maintain access logs and role-based access control for the log archive.
Monitoring and error handling
- Instrument the automation with metrics: jobs started/completed/failed, average collection time, and payload sizes.
- Alert on repeated failures for specific devices or error classes (network unreachable, agent not installed).
- Implement retries with backoff and a dead-letter queue for problematic devices.
Security considerations
- Limit MaaS360 API credentials to the minimum required scopes.
- Rotate credentials and store them securely (vault/KMS).
- Sanitize and redact sensitive PII from logs if necessary before wider access.
- Ensure transport uses TLS and verify certificates when downloading exports.
Operational tips and pitfalls
- Test automation on a small pilot group before full rollout.
- Watch API rate limits and implement throttling.
- Be mindful of device storage and battery impact when scheduling frequent collections.
- For iOS, some logs may be larger or require user consent—verify agent capabilities and platform constraints.
- Keep versioned runbooks documenting how to manually gather logs if automation fails.
Example use cases
- Reactive incident response: trigger collections when SIEM detects suspicious activity.
- Scheduled health checks: nightly collections from a sample of devices for proactive diagnostics.
- Compliance: preserve logs tied to security investigations with immutable storage.
Conclusion
Automating log collection with MaaS360 Log Viewer combines the visibility of device diagnostics with the efficiency of automation. By building a reliable pipeline—authentication, issuing remote actions, downloading exports, parsing and storing logs—you reduce time to resolution and create an auditable, scalable process. Start small, handle errors and rate limits, and expand once your workflow proves reliable.