| agent: | Auto Exec |
What is an "Expert"? How do we create our own expert?
Add credentials for various integrations
Managing workspaces and access control
DagKnows Architecture Overview
Setting up SSO via Azure AD for Dagknows
Enable "Auto Exec" and "Send Execution Result to LLM" in "Adjust Settings" if desired
(Optionally) Add ubuntu user to docker group and refresh group membership
Deployment of an EKS Cluster with Worker Nodes in AWS
Adding, Deleting, Listing DagKnows Proxy credentials or key-value pairs
Comprehensive AWS Security and Compliance Evaluation Workflow (SOC2 Super Runbook)
AWS EKS Version Update 1.29 to 1.30 via terraform
Instruction to allow WinRM connection
MSP Usecase: User Onboarding Azure + M365
Post a message to a Slack channel
How to debug a kafka cluster and kafka topics?
Open VPN Troubleshooting (Powershell)
Execute a simple task on the proxy
Assign the proxy role to a user
Create roles to access credentials in proxy
Install OpenVPN client on Windows laptop
Setup Kubernetes kubectl and Minikube on Ubuntu 22.04 LTS
Install Prometheus and Grafana on the minikube cluster on EC2 instance in the monitoring namespace
update the EKS versions in different clusters
AI agent session 2024-09-12T09:36:14-07:00 by Sarang Dharmapurikar
Parse EDN content and give a JSON out
Check whether a user is there on Azure AD and if the user account status is enabled
Get the input parameters of a Jenkins pipeline
AWS CloudTrail Configuration and Encryption Verification
The workflow involves evaluating all AWS CloudTrail configurations to ensure they are set up correctly. A key focus is on verifying that server-side encryption with AWS Key Management Service (SSE-KMS) is enabled. This ensures that all logs are securely encrypted, enhancing the security and compliance of the AWS environment. The process helps in maintaining the integrity and confidentiality of the log data. By confirming these settings, the workflow supports robust security practices within the AWS infrastructure.
- 1BXzgQWqZlphs6v2WeM2ZEvaluate all AWS CloudTrail configurations and verify SSE-KMS encryption
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Evaluate all AWS CloudTrail configurations and verify SSE-KMS encryption
There was a problem that the LLM was not able to address. Please rephrase your prompt and try again.This script evaluates AWS CloudTrail configurations to verify if SSE-KMS encryption is enabled and tabulates the compliance results.
inputsoutputsimport boto3 import json # Initialize AWS CloudTrail client aws_access_key_id = getEnvVar('AWS_ACCESS_KEY_ID') aws_secret_access_key = getEnvVar('AWS_SECRET_ACCESS_KEY') client = boto3.client('cloudtrail', region_name=region_name, aws_access_key_id=aws_access_key_id, aws_secret_access_key=aws_secret_access_key) # Fetch all trails response = client.describe_trails() trails = response.get('trailList', []) # Evaluate each trail for SSE-KMS encryption compliance_results = [] for trail in trails: trail_name = trail.get('Name', 'Unknown') kms_key_id = trail.get('KmsKeyId') if kms_key_id: compliance_results.append({'TrailName': trail_name, 'Compliance': 'COMPLIANT'}) else: compliance_results.append({'TrailName': trail_name, 'Compliance': 'NON_COMPLIANT'}) # Tabulate the results table = context.newtable() table.num_rows = len(compliance_results) + 1 # +1 for header table.num_cols = 2 table.title = "CloudTrail SSE-KMS Compliance" table.has_header_row = True table.setval(0, 0, "Trail Name") table.setval(0, 1, "Compliance") for i, result in enumerate(compliance_results, start=1): table.setval(i, 0, result['TrailName']) table.setval(i, 1, result['Compliance']) print("Compliance results tabulated successfully.")copied1