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    Collect DigiCert audit logs

    Supported in:

    This document explains how to ingest DigiCert audit logs to Google Security Operations using Google Cloud Storage. DigiCert CertCentral is a certificate lifecycle management platform that provides audit logs for certificate operations, user activities, and administrative actions.

    Before you begin

    Ensure that you have the following prerequisites:

    • A Google SecOps instance
    • A GCP project with Cloud Storage API enabled
    • Permissions to create and manage GCS buckets
    • Permissions to manage IAM policies on GCS buckets
    • Permissions to create Cloud Run services, Pub/Sub topics, and Cloud Scheduler jobs
    • Privileged access to DigiCert CertCentral (API key with Administrator role)

    Create Google Cloud Storage bucket

    1. Go to the Google Cloud Console .
    2. Select your project or create a new one.
    3. In the navigation menu, go to Cloud Storage > Buckets.
    4. Click Create bucket.

    5. Provide the following configuration details:

      Setting Value
      Name your bucket Enter a globally unique name (for example, digicert-logs )
      Location type Choose based on your needs (Region, Dual-region, Multi-region)
      Location Select the location (for example, us-central1 )
      Storage class Standard (recommended for frequently accessed logs)
      Access control Uniform (recommended)
      Protection tools Optional: Enable object versioning or retention policy

    6. Click Create.

    Collect DigiCert API credentials

    Get DigiCert API key

    1. Sign in to DigiCert CertCentral.
    2. Go to Account > API Keys.
    3. Click Create API Key.
    4. Provide the following configuration details:
      • Name: Enter a descriptive name (for example, Chronicle Integration ).
      • Role: Select Administrator.
    5. Click Create.
    6. Copy and save the API key ( X-DC-DEVKEY ). This value will not be shown again.

    Get DigiCert Report ID

    1. In DigiCert CertCentral, go to Reports > Report Library.
    2. Click Create Report.
    3. Provide the following configuration details:
      • Report Type: Select Audit log.
      • Format: Select JSON.
      • Name: Enter a descriptive name (for example, Chronicle Audit Logs ).
    4. Click Create.

    5. Copy and save the Report ID(UUID format).

    The Cloud Run function needs a service account with permissions to write to GCS bucket and be invoked by Pub/Sub.

    1. In the GCP Console, go to IAM & Admin > Service Accounts.
    2. Click Create Service Account.
    3. Provide the following configuration details:
      • Service account name: Enter digicert-logs-collector-sa .
      • Service account description: Enter Service account for Cloud Run function to collect DigiCert audit logs .
    4. Click Create and Continue.
    5. In the Grant this service account access to projectsection, add the following roles:
      1. Click Select a role.
      2. Search for and select Storage Object Admin.
      3. Click + Add another role.
      4. Search for and select Cloud Run Invoker.
      5. Click + Add another role.
      6. Search for and select Cloud Functions Invoker.
    6. Click Continue.
    7. Click Done.

    These roles are required for:

    • Storage Object Admin: Write logs to GCS bucket and manage state files
    • Cloud Run Invoker: Allow Pub/Sub to invoke the function
    • Cloud Functions Invoker: Allow function invocation

    Grant IAM permissions on GCS bucket

    Grant the service account write permissions on the GCS bucket:

    1. Go to Cloud Storage > Buckets.
    2. Click your bucket name.
    3. Go to the Permissionstab.
    4. Click Grant access.
    5. Provide the following configuration details:
      • Add principals: Enter the service account email (for example, digicert-logs-collector-sa@PROJECT_ID.iam.gserviceaccount.com ).
      • Assign roles: Select Storage Object Admin.
    6. Click Save.

    Create Pub/Sub topic

    Create a Pub/Sub topic that Cloud Scheduler will publish to and the Cloud Run function will subscribe to.

    1. In the GCP Console, go to Pub/Sub > Topics.
    2. Click Create topic.
    3. Provide the following configuration details:
      • Topic ID: Enter digicert-audit-trigger .
      • Leave other settings as default.
    4. Click Create.

    Create Cloud Run function to collect logs

    The Cloud Run function is triggered by Pub/Sub messages from Cloud Scheduler to fetch logs from DigiCert API and writes them to GCS.

    1. In the GCP Console, go to Cloud Run.
    2. Click Create service.
    3. Select Function(use an inline editor to create a function).

    4. In the Configuresection, provide the following configuration details:

      Setting Value
      Service name digicert-audit-logs-collector
      Region Select region matching your GCS bucket (for example, us-central1 )
      Runtime Select Python 3.12or later

    5. In the Trigger (optional)section:

      1. Click + Add trigger.
      2. Select Cloud Pub/Sub.
      3. In Select a Cloud Pub/Sub topic, choose the Pub/Sub topic ( digicert-audit-trigger ).
      4. Click Save.

    6. In the Authenticationsection:

      1. Select Require authentication.
      2. Check Identity and Access Management (IAM).

    7. Scroll down and expand Containers, Networking, Security.

    8. Go to the Securitytab:

      • Service account: Select the service account ( digicert-logs-collector-sa ).

    9. Go to the Containerstab:

      1. Click Variables & Secrets.
      2. Click + Add variablefor each environment variable:
      Variable Name Example Value
      GCS_BUCKET digicert-logs
      GCS_PREFIX digicert/logs
      STATE_KEY digicert/logs/state.json
      DIGICERT_API_KEY xxxxxxxxxxxxxxxxxxxxxxxx
      DIGICERT_REPORT_ID 88de5e19-ec57-4d70-865d-df953b062574
      REQUEST_TIMEOUT 30
      POLL_INTERVAL 10
      MAX_WAIT_SECONDS 300

    10. Scroll down in the Variables & Secretstab to Requests:

      • Request timeout: Enter 900 seconds (15 minutes).

    11. Go to the Settingstab in Containers:

      • In the Resourcessection:
        • Memory: Select 512 MiBor higher.
        • CPU: Select 1.
      • Click Done.

    12. Scroll to Execution environment:

      • Select Default(recommended).

    13. In the Revision scalingsection:

      • Minimum number of instances: Enter 0 .
      • Maximum number of instances: Enter 100 (or adjust based on expected load).

    14. Click Create.

    15. Wait for the service to be created (1-2 minutes).

    16. After the service is created, the inline code editoropens automatically.

    Add function code

    1. Enter mainin Function entry point

    2. In the inline code editor, create two files:

      • First file: main.py:
        import 
  
 functions_framework 
 from 
  
 google.cloud 
  
 import 
  storage 
 
 import 
  
 json 
 import 
  
 os 
 import 
  
 urllib3 
 from 
  
 datetime 
  
 import 
 datetime 
 , 
 timezone 
 , 
 timedelta 
 import 
  
 time 
 import 
  
 io 
 import 
  
 gzip 
 import 
  
 zipfile 
 import 
  
 uuid 
 # Initialize HTTP client 
 http 
 = 
 urllib3 
 . 
 PoolManager 
 () 
 # Initialize Storage client 
 storage_client 
 = 
  storage 
 
 . 
  Client 
 
 () 
 API_BASE 
 = 
 "https://api.digicert.com/reports/v1" 
 USER_AGENT 
 = 
 "secops-digicert-reports/1.0" 
 @functions_framework 
 . 
 cloud_event 
 def 
  
 main 
 ( 
 cloud_event 
 ): 
  
 """ 
 Cloud Run function triggered by Pub/Sub to fetch DigiCert audit logs and write to GCS. 
 Args: 
 cloud_event: CloudEvent object containing Pub/Sub message 
 """ 
 # Get environment variables 
 bucket_name 
 = 
 os 
 . 
 environ 
 . 
 get 
 ( 
 'GCS_BUCKET' 
 ) 
 prefix 
 = 
 os 
 . 
 environ 
 . 
 get 
 ( 
 'GCS_PREFIX' 
 , 
 'digicert/logs' 
 ) 
 . 
 rstrip 
 ( 
 '/' 
 ) 
 state_key 
 = 
 os 
 . 
 environ 
 . 
 get 
 ( 
 'STATE_KEY' 
 , 
 f 
 ' 
 { 
 prefix 
 } 
 /state.json' 
 ) 
 api_key 
 = 
 os 
 . 
 environ 
 . 
 get 
 ( 
 'DIGICERT_API_KEY' 
 ) 
 report_id 
 = 
 os 
 . 
 environ 
 . 
 get 
 ( 
 'DIGICERT_REPORT_ID' 
 ) 
 max_wait 
 = 
 int 
 ( 
 os 
 . 
 environ 
 . 
 get 
 ( 
 'MAX_WAIT_SECONDS' 
 , 
 '300' 
 )) 
 poll_int 
 = 
 int 
 ( 
 os 
 . 
 environ 
 . 
 get 
 ( 
 'POLL_INTERVAL' 
 , 
 '10' 
 )) 
 timeout 
 = 
 int 
 ( 
 os 
 . 
 environ 
 . 
 get 
 ( 
 'REQUEST_TIMEOUT' 
 , 
 '30' 
 )) 
 if 
 not 
 all 
 ([ 
 bucket_name 
 , 
 api_key 
 , 
 report_id 
 ]): 
 print 
 ( 
 'Error: Missing required environment variables' 
 ) 
 return 
 try 
 : 
 # Get GCS bucket 
 bucket 
 = 
 storage_client 
 . 
  bucket 
 
 ( 
 bucket_name 
 ) 
 # Load state 
 state 
 = 
 load_state 
 ( 
 bucket 
 , 
 state_key 
 ) 
 last_run 
 = 
  state 
 
 . 
 get 
 ( 
 'last_run_id' 
 ) 
 # Start report run 
 started 
 = 
 datetime 
 . 
 now 
 ( 
 timezone 
 . 
 utc 
 ) 
 start_report_run 
 ( 
 api_key 
 , 
 report_id 
 , 
 timeout 
 ) 
 # Wait for report to be ready 
 run_id 
 = 
 find_ready_run 
 ( 
 api_key 
 , 
 report_id 
 , 
 started 
 , 
 timeout 
 , 
 max_wait 
 , 
 poll_int 
 ) 
 # Skip if same run as last time 
 if 
 last_run 
 and 
 last_run 
 == 
 run_id 
 : 
 print 
 ( 
 f 
 'Skipping duplicate run: 
 { 
 run_id 
 } 
 ' 
 ) 
 return 
 # Get report data 
 rows 
 = 
 get_json_rows 
 ( 
 api_key 
 , 
 report_id 
 , 
 run_id 
 , 
 timeout 
 ) 
 # Write to GCS 
 key 
 = 
 write_ndjson_gz 
 ( 
 bucket 
 , 
 prefix 
 , 
 rows 
 , 
 run_id 
 ) 
 # Update state 
 save_state 
 ( 
 bucket 
 , 
 state_key 
 , 
 { 
 'last_run_id' 
 : 
 run_id 
 , 
 'last_success_at' 
 : 
 datetime 
 . 
 now 
 ( 
 timezone 
 . 
 utc 
 ) 
 . 
 isoformat 
 (), 
 'last_s3_key' 
 : 
 key 
 , 
 'rows_count' 
 : 
 len 
 ( 
 rows 
 ) 
 }) 
 print 
 ( 
 f 
 'Successfully processed 
 { 
 len 
 ( 
 rows 
 ) 
 } 
 logs to 
 { 
 key 
 } 
 ' 
 ) 
 except 
 Exception 
 as 
 e 
 : 
 print 
 ( 
 f 
 'Error processing logs: 
 { 
 str 
 ( 
 e 
 ) 
 } 
 ' 
 ) 
 raise 
 def 
  
 http_request 
 ( 
 method 
 , 
 url 
 , 
 api_key 
 , 
 body 
 = 
 None 
 , 
 timeout 
 = 
 30 
 , 
 max_retries 
 = 
 5 
 ): 
  
 """Make HTTP request with retry logic.""" 
 headers 
 = 
 { 
 'X-DC-DEVKEY' 
 : 
 api_key 
 , 
 'Content-Type' 
 : 
 'application/json' 
 , 
 'User-Agent' 
 : 
 USER_AGENT 
 } 
 attempt 
 , 
 backoff 
 = 
 0 
 , 
 1.0 
 while 
 True 
 : 
 try 
 : 
 response 
 = 
 http 
 . 
 request 
 ( 
 method 
 , 
 url 
 , 
 headers 
 = 
 headers 
 , 
 body 
 = 
 body 
 , 
 timeout 
 = 
 timeout 
 ) 
 status 
 = 
 response 
 . 
 status 
 # Retry on server errors 
 if 
 500 
< = 
 status 
< = 
 599 
 and 
 attempt 
< max_retries 
 : 
 attempt 
 += 
 1 
 time 
 . 
 sleep 
 ( 
 backoff 
 ) 
 backoff 
 *= 
 2 
 continue 
 # Retry on rate limit 
 if 
 status 
 == 
 429 
 and 
 attempt 
< max_retries 
 : 
 retry_after 
 = 
 response 
 . 
 headers 
 . 
 get 
 ( 
 'Retry-After' 
 ) 
 delay 
 = 
 float 
 ( 
 retry_after 
 ) 
 if 
 retry_after 
 and 
 retry_after 
 . 
 isdigit 
 () 
 else 
 backoff 
 attempt 
 += 
 1 
 time 
 . 
 sleep 
 ( 
 delay 
 ) 
 backoff 
 *= 
 2 
 continue 
 if 
 status 
 not 
 in 
 ( 
 200 
 , 
 201 
 ): 
 raise 
 RuntimeError 
 ( 
 f 
 'HTTP 
 { 
 status 
 } 
 : 
 { 
 response 
 . 
 data 
 [: 
 200 
 ] 
 } 
 ' 
 ) 
 return 
 status 
 , 
 response 
 . 
 headers 
 , 
 response 
 . 
 data 
 except 
 urllib3 
 . 
  exceptions 
 
 . 
 HTTPError 
 as 
 e 
 : 
 if 
 attempt 
< max_retries 
 : 
 attempt 
 += 
 1 
 time 
 . 
 sleep 
 ( 
 backoff 
 ) 
 backoff 
 *= 
 2 
 continue 
 raise 
 def 
  
 start_report_run 
 ( 
 api_key 
 , 
 report_id 
 , 
 timeout 
 ): 
  
 """Start a new report run.""" 
 status 
 , 
 _ 
 , 
 body 
 = 
 http_request 
 ( 
 'POST' 
 , 
 f 
 ' 
 { 
 API_BASE 
 } 
 /report/ 
 { 
 report_id 
 } 
 /run' 
 , 
 api_key 
 , 
 b 
 ' 
 {} 
 ' 
 , 
 timeout 
 ) 
 if 
 status 
 not 
 in 
 ( 
 200 
 , 
 201 
 ): 
 raise 
 RuntimeError 
 ( 
 f 
 'Start run failed: 
 { 
 status 
 } 
  
 { 
 body 
 [: 
 200 
 ] 
 } 
 ' 
 ) 
 def 
  
 list_report_history 
 ( 
 api_key 
 , 
 status_filter 
 = 
 None 
 , 
 report_type 
 = 
 None 
 , 
 limit 
 = 
 100 
 , 
 timeout 
 = 
 30 
 ): 
  
 """List report history.""" 
 params 
 = 
 { 
 'limit' 
 : 
 str 
 ( 
 limit 
 ), 
 'offset' 
 : 
 '0' 
 , 
 'sort_by' 
 : 
 'report_start_date' 
 , 
 'sort_direction' 
 : 
 'DESC' 
 } 
 if 
 status_filter 
 : 
 params 
 [ 
 'status' 
 ] 
 = 
 status_filter 
 if 
 report_type 
 : 
 params 
 [ 
 'report_type' 
 ] 
 = 
 report_type 
 query_string 
 = 
 '&' 
 . 
 join 
 ([ 
 f 
 ' 
 { 
 k 
 } 
 = 
 { 
 v 
 } 
 ' 
 for 
 k 
 , 
 v 
 in 
 params 
 . 
 items 
 ()]) 
 url 
 = 
 f 
 ' 
 { 
 API_BASE 
 } 
 /report/history? 
 { 
 query_string 
 } 
 ' 
 status 
 , 
 _ 
 , 
 body 
 = 
 http_request 
 ( 
 'GET' 
 , 
 url 
 , 
 api_key 
 , 
 timeout 
 = 
 timeout 
 ) 
 if 
 status 
 != 
 200 
 : 
 raise 
 RuntimeError 
 ( 
 f 
 'History failed: 
 { 
 status 
 } 
  
 { 
 body 
 [: 
 200 
 ] 
 } 
 ' 
 ) 
 return 
 json 
 . 
 loads 
 ( 
 body 
 . 
 decode 
 ( 
 'utf-8' 
 )) 
 def 
  
 find_ready_run 
 ( 
 api_key 
 , 
 report_id 
 , 
 started_not_before 
 , 
 timeout 
 , 
 max_wait_seconds 
 , 
 poll_interval 
 ): 
  
 """Find a ready report run.""" 
 deadline 
 = 
 time 
 . 
 time 
 () 
 + 
 max_wait_seconds 
 while 
 time 
 . 
 time 
 () 
< deadline 
 : 
 hist 
 = 
 list_report_history 
 ( 
 api_key 
 , 
 status_filter 
 = 
 'READY' 
 , 
 limit 
 = 
 200 
 , 
 timeout 
 = 
 timeout 
 ) 
 . 
 get 
 ( 
 'report_history' 
 , 
 []) 
 for 
 item 
 in 
 hist 
 : 
 if 
 item 
 . 
 get 
 ( 
 'report_identifier' 
 ) 
 != 
 report_id 
 : 
 continue 
 if 
 not 
 item 
 . 
 get 
 ( 
 'report_run_identifier' 
 ): 
 continue 
 try 
 : 
 rsd 
 = 
 datetime 
 . 
 strptime 
 ( 
 item 
 . 
 get 
 ( 
 'report_start_date' 
 , 
 '' 
 ), 
 '%Y-%m- 
 %d 
 %H:%M:%S' 
 ) 
 . 
 replace 
 ( 
 tzinfo 
 = 
 timezone 
 . 
 utc 
 ) 
 except 
 Exception 
 : 
 rsd 
 = 
 started_not_before 
 if 
 rsd 
 + 
 timedelta 
 ( 
 seconds 
 = 
 60 
 ) 
> = 
 started_not_before 
 : 
 return 
 item 
 [ 
 'report_run_identifier' 
 ] 
 time 
 . 
 sleep 
 ( 
 poll_interval 
 ) 
 raise 
 TimeoutError 
 ( 
 'READY run not found in time' 
 ) 
 def 
  
 get_json_rows 
 ( 
 api_key 
 , 
 report_id 
 , 
 run_id 
 , 
 timeout 
 ): 
  
 """Get JSON rows from report.""" 
 status 
 , 
 headers 
 , 
 body 
 = 
 http_request 
 ( 
 'GET' 
 , 
 f 
 ' 
 { 
 API_BASE 
 } 
 /report/ 
 { 
 report_id 
 } 
 / 
 { 
 run_id 
 } 
 /json' 
 , 
 api_key 
 , 
 timeout 
 = 
 timeout 
 ) 
 if 
 status 
 != 
 200 
 : 
 raise 
 RuntimeError 
 ( 
 f 
 'Get JSON failed: 
 { 
 status 
 } 
  
 { 
 body 
 [: 
 200 
 ] 
 } 
 ' 
 ) 
 # Check if response is ZIP 
 content_type 
 = 
 headers 
 . 
 get 
 ( 
 'content-type' 
 , 
 '' 
 ) 
 . 
 lower 
 () 
 if 
 'application/zip' 
 in 
 content_type 
 or 
 body 
 [: 
 2 
 ] 
 == 
 b 
 'PK' 
 : 
 with 
 zipfile 
 . 
 ZipFile 
 ( 
 io 
 . 
 BytesIO 
 ( 
 body 
 )) 
 as 
 zf 
 : 
 json_files 
 = 
 [ 
 n 
 for 
 n 
 in 
 zf 
 . 
 namelist 
 () 
 if 
 n 
 . 
 lower 
 () 
 . 
 endswith 
 ( 
 '.json' 
 )] 
 if 
 not 
 json_files 
 : 
 raise 
 RuntimeError 
 ( 
 'ZIP has no JSON' 
 ) 
 rows 
 = 
 json 
 . 
 loads 
 ( 
 zf 
 . 
 read 
 ( 
 json_files 
 [ 
 0 
 ]) 
 . 
 decode 
 ( 
 'utf-8' 
 )) 
 else 
 : 
 rows 
 = 
 json 
 . 
 loads 
 ( 
 body 
 . 
 decode 
 ( 
 'utf-8' 
 )) 
 if 
 not 
 isinstance 
 ( 
 rows 
 , 
 list 
 ): 
 raise 
 RuntimeError 
 ( 
 'Unexpected JSON format' 
 ) 
 return 
 rows 
 def 
  
 write_ndjson_gz 
 ( 
 bucket 
 , 
 prefix 
 , 
 rows 
 , 
 run_id 
 ): 
  
 """Write NDJSON gzipped file to GCS.""" 
 ts 
 = 
 datetime 
 . 
 now 
 ( 
 timezone 
 . 
 utc 
 ) 
 . 
 strftime 
 ( 
 '%Y/%m/ 
 %d 
 /%H%M%S' 
 ) 
 key 
 = 
 f 
 ' 
 { 
 prefix 
 } 
 / 
 { 
 ts 
 } 
 -digicert-audit- 
 { 
 run_id 
 [: 
 8 
 ] 
 } 
 - 
 { 
 uuid 
 . 
 uuid4 
 () 
 . 
 hex 
 } 
 .json.gz' 
 buf 
 = 
 io 
 . 
 BytesIO 
 () 
 with 
 gzip 
 . 
 GzipFile 
 ( 
 fileobj 
 = 
 buf 
 , 
 mode 
 = 
 'wb' 
 ) 
 as 
 gz 
 : 
 for 
 r 
 in 
 rows 
 : 
 gz 
 . 
 write 
 (( 
 json 
 . 
 dumps 
 ( 
 r 
 , 
 separators 
 = 
 ( 
 ',' 
 , 
 ':' 
 )) 
 + 
 ' 
 \n 
 ' 
 ) 
 . 
 encode 
 ( 
 'utf-8' 
 )) 
 blob 
 = 
 bucket 
 . 
 blob 
 ( 
 key 
 ) 
 blob 
 . 
  upload_from_string 
 
 ( 
 buf 
 . 
 getvalue 
 (), 
 content_type 
 = 
 'application/x-ndjson' 
 , 
 content_encoding 
 = 
 'gzip' 
 ) 
 return 
 key 
 def 
  
 load_state 
 ( 
 bucket 
 , 
 key 
 ): 
  
 """Load state from GCS.""" 
 try 
 : 
 blob 
 = 
 bucket 
 . 
 blob 
 ( 
 key 
 ) 
 if 
 blob 
 . 
 exists 
 (): 
 state_data 
 = 
 blob 
 . 
  download_as_text 
 
 () 
 return 
 json 
 . 
 loads 
 ( 
 state_data 
 ) 
 except 
 Exception 
 as 
 e 
 : 
 print 
 ( 
 f 
 'Warning: Could not load state: 
 { 
 str 
 ( 
 e 
 ) 
 } 
 ' 
 ) 
 return 
 {} 
 def 
  
 save_state 
 ( 
 bucket 
 , 
 key 
 , 
 state 
 ): 
  
 """Save state to GCS.""" 
 try 
 : 
 blob 
 = 
 bucket 
 . 
 blob 
 ( 
 key 
 ) 
 blob 
 . 
  upload_from_string 
 
 ( 
 json 
 . 
 dumps 
 ( 
 state 
 ), 
 content_type 
 = 
 'application/json' 
 ) 
 except 
 Exception 
 as 
 e 
 : 
 print 
 ( 
 f 
 'Warning: Could not save state: 
 { 
 str 
 ( 
 e 
 ) 
 } 
 ' 
 )
      • Second file: requirements.txt:
        functions 
 - 
 framework 
 == 
 3 
 .* 
 google 
 - 
 cloud 
 - 
 storage 
 == 
 2 
 .* 
 urllib3 
> = 
 2.0 
 . 
 0

    3. Click Deployto save and deploy the function.

    4. Wait for deployment to complete (2-3 minutes).

    Create Cloud Scheduler job

    Cloud Scheduler publishes messages to the Pub/Sub topic at regular intervals, triggering the Cloud Run function.

    1. In the GCP Console, go to Cloud Scheduler.
    2. Click Create Job.

    3. Provide the following configuration details:

      Setting Value
      Name digicert-audit-hourly
      Region Select same region as Cloud Run function
      Frequency 0 * * * * (every hour, on the hour)
      Timezone Select timezone (UTC recommended)
      Target type Pub/Sub
      Topic Select the Pub/Sub topic ( digicert-audit-trigger )
      Message body {} (empty JSON object)

    4. Click Create.

    Schedule frequency options

    • Choose frequency based on log volume and latency requirements:

      Frequency Cron Expression Use Case
      Every 5 minutes
      		 */5 * * * * High-volume, low-latency
      Every 15 minutes
      		 */15 * * * * Medium volume
      Every hour
      		 0 * * * * Standard (recommended)
      Every 6 hours
      		 0 */6 * * * Low volume, batch processing
      Daily
      		 0 0 * * * Historical data collection

    Test the scheduler job

    1. In the Cloud Schedulerconsole, find your job.
    2. Click Force runto trigger manually.
    3. Wait a few seconds and go to Cloud Run > Services > digicert-audit-logs-collector > Logs.
    4. Verify the function executed successfully.
    5. Check the GCS bucket to confirm logs were written.

    Google SecOps uses a unique service account to read data from your GCS bucket. You must grant this service account access to your bucket.

    1. Go to SIEM Settings > Feeds.
    2. Click Add New Feed.
    3. Click Configure a single feed.
    4. In the Feed namefield, enter a name for the feed (for example, DigiCert Audit Logs ).
    5. Select Google Cloud Storage V2as the Source type.
    6. Select Digicertas the Log type.

    7. Click Get Service Account. A unique service account email will be displayed, for example:

        chronicle 
 - 
 12345678 
 @chronicle 
 - 
 gcp 
 - 
 prod 
 . 
 iam 
 . 
 gserviceaccount 
 . 
 com

    8. Copy this email address for use in the next step.

    The Google SecOps service account needs Storage Object Viewerrole on your GCS bucket.

    1. Go to Cloud Storage > Buckets.
    2. Click your bucket name.
    3. Go to the Permissionstab.
    4. Click Grant access.
    5. Provide the following configuration details:
      • Add principals: Paste the Google SecOps service account email.
      • Assign roles: Select Storage Object Viewer.

    6. Click Save.

    Configure a feed in Google SecOps to ingest DigiCert logs

    1. Go to SIEM Settings > Feeds.
    2. Click Add New Feed.
    3. Click Configure a single feed.
    4. In the Feed namefield, enter a name for the feed (for example, DigiCert Audit Logs ).
    5. Select Google Cloud Storage V2as the Source type.
    6. Select Digicertas the Log type.
    7. Click Next.

    8. Specify values for the following input parameters:

      • Storage bucket URL: Enter the GCS bucket URI with the prefix path:

         gs://digicert-logs/digicert/logs/

        • Replace:

          • digicert-logs : Your GCS bucket name.
          • digicert/logs : Prefix/folder path where logs are stored.

      • Source deletion option: Select the deletion option according to your preference:

        • Never: Never deletes any files after transfers (recommended for testing).
        • Delete transferred files: Deletes files after successful transfer.

        • Delete transferred files and empty directories: Deletes files and empty directories after successful transfer.

      • Maximum File Age: Include files modified in the last number of days. Default is 180 days.

      • Asset namespace: The asset namespace .

      • Ingestion labels: The label to be applied to the events from this feed.

    9. Click Next.

    10. Review your new feed configuration in the Finalizescreen, and then click Submit.

    Need more help? Get answers from Community members and Google SecOps professionals.

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