For continuous monitoring and detecting unusual configuration changes, I would suggest you look into AWS Config.

AWS Config enables you to assess, audit, and evaluate the configurations of your AWS resources. Config continuously monitors and records your AWS resource configurations and allows you to automate the evaluation of recorded configurations against desired configurations. Here is a list of supported AWS resources types and resource relationships with AWS Config https://docs.aws.amazon.com/config/latest/developerguide/resource-config-reference.html

Also as of Nov, 2019 - AWS Config launches support for third-party resources. You can now publish the configuration of third-party resources, such as GitHub repositories, Microsoft Active Directory resources, or any on-premises server into AWS Config using the new API. Here is more detail: https://docs.aws.amazon.com/config/latest/developerguide/customresources.html

If you have multiple AWS Account in your organization and want to detect changes there: https://docs.aws.amazon.com/config/latest/developerguide/aggregate-data.html

Lastly, if you already use Splunk Cloud in your enterprise and are looking for a consolidated view then, AWS Config is supported by Splunk Cloud as per their documentation too. https://aws.amazon.com/marketplace/pp/Splunk-Inc-Splunk-Cloud/B06XK299KV https://aws.amazon.com/marketplace/pp/Splunk-Inc-Splunk-Cloud/B06XK299KV

While it won't detect events as they happen a good stop gap would be to define your infrastructure config using terraform. You can then periodically run the terraform config against your environment and alert if there are any changes.

Consider using a combination of Netflix Security Monkey and AWS Guard Duty.

You can achieve automated detection and alerting, as well as automated recovery based on policies with these tools.

For instance, you could detect SecurityGroup rule changes that allow unrestricted egress from EC2 instances and then revert those changes automatically.

It's unclear from your post whether you want to detect events within the Splunk Cloud infrastructure or if you want to detect events indicated in data going to Splunk using the Splunk capabilities. If the latter, then Splunk has extremely rich capabilities in their query language and integrated alerting functions. With Splunk you can also run arbitrary Python scripts in response to certain events, so what you can't analyze and alert on with native functionality or plugins, you could write code to achieve.

Well there are clear advantages of using either tools, it all boils down to what exactly are you trying to achieve with this i.e do you want to proactive monitoring or do you want debug an incident/issue. Splunk definitely is superior in terms of proactively monitoring your logs for unusal events, but getting the cloudtrail logs across to splunk would require some not so straight forward setup (Splunk has a blueprint for this setup which uses AWS kinesis/Firehose). Cloudtrail on the other had is available out of the box from AWS, the setup is quite simple and straight forward. But analysing the log could require you setup Glue crawlers and you might have to use AWS Athena to run SQL Like query.

Refer: https://docs.aws.amazon.com/athena/latest/ug/cloudtrail-logs.html

In my personal experience the cost/effort involved in setting up splunk is not worth it for smaller workloads, whereas the AWS Cloudtrail/Glue/Athena would be less expensive setup(comparatively).

Alternatively you could look at something like sumologic, which has better integration with cloudtrail as opposed to splunk. Hope that helps.

I'd recommend using CloudTrail, it helped me a lot. But depending on your situation I'd recommed building a custom solution(like aws amazon-ssm-agent) which on configuration change makes an API call and logs them in grafana or kibana.

For our Predictive Analytics platform, we have used both Grafana and Kibana

• Grafana based demo video: https://www.youtube.com/watch?v=tdTB2AcU4Sg
• Kibana based reporting screenshot: https://imgur.com/vuVvZKN

Kibana has predictions and ML algorithms support, so if you need them, you may be better off with Kibana . The multi-variate analysis features it provide are very unique (not available in Grafana).

For everything else, definitely Grafana . Especially the number of supported data sources, and plugins clearly makes Grafana a winner (in just visualization and reporting sense). Creating your own plugin is also very easy. The top pros of Grafana (which it does better than Kibana ) are:

• Creating and organizing visualization panels
• Templating the panels on dashboards for repetetive tasks
• Realtime monitoring, filtering of charts based on conditions and variables
• Export / Import in JSON format (that allows you to version and save your dashboard as part of git)

I use both Kibana and Grafana on my workplace: Kibana for logging and Grafana for monitoring. Since you already work with Elasticsearch, I think Kibana is the safest choice in terms of ease of use and variety of messages it can manage, while Grafana has still (in my opinion) a strong link to metrics

After looking for a way to monitor or at least get a better overview of our infrastructure, we found out that Grafana (which I previously only used in ELK stacks) has a plugin available to fully integrate with Amazon CloudWatch . Which makes it way better for our use-case than the offer of the different competitors (most of them are even paid). There is also a CloudFlare plugin available, the platform we use to serve our DNS requests. Although we are a big fan of https://smashing.github.io/ (previously dashing), for now we are starting with Grafana .

I use Kibana because it ships with the ELK stack. I don't find it as powerful as Splunk however it is light years above grepping through log files. We previously used Grafana but found it to be annoying to maintain a separate tool outside of the ELK stack. We were able to get everything we needed from Kibana.

Kibana should be sufficient in this architecture for decent analytics, if stronger metrics is needed then combine with Grafana. Datadog also offers nice overview but there's no need for it in this case unless you need more monitoring and alerting (and more technicalities).

I use Grafana because it is without a doubt the best way to visualize metrics

@Kibana, of course, because @Grafana looks like amateur sort of solution, crammed with query builder grouping aggregates, but in essence, as recommended by CERN - KIbana is the corporate (startup vectored) decision.

Furthermore, @Kibana comes with complexity adhering ELK stack, whereas @InfluxDB + @Grafana & co. recently have become sophisticated development conglomerate instead of advancing towards a understandable installation step by step inheritance.