I’ve been asked to join PigeonLine as a Board Advisor for cyber security. I’m excited to be able to contribute to the success of this promising startup.
PigeonLine is a fast growing AI development and consulting company that builds tools to solve common enterprise problems. Their customers include the UAE Prime Ministers Office, the Bank of Canada, the London School of Economics, among others.
Building accessible AI tools to empower people should go hand-in-hand with protecting their privacy and preserving the security of their information.
I like the company’s user-centric approach and the fact that data privacy is one of their core values. I’m thrilled to be part of their journey to push the boundaries of human-machine interaction to solve common decision-making problems for enterprises and governments.
This AWS service certainly has its imperfections (e.g. it doesn’t support all AWS resources) but it is easy to set up and can be quite useful too. When you first enable it, Config will analyse the resources in your account and make the summary available to you in a dashboard (example below). It’s a regional service, so you might want to enable it in all active regions.
Config, however, doesn’t stop there. You can now use this snapshot as a reference point and track all changes to your resources on a timeline. It can be useful when you need to analyse historical records, demonstrate compliance or gain visibility in your change management practices. It can also notify you of any configuration changes if you set up SNS notifications.
Config rules allow you to continuously track compliance with various baselines. AWS provide quite a few out of the box and you can create your own to tailor to the specific environment you operate in. You have to pay separately for rules, so I encourage you to check out pricing first.
As with some other AWS services, you can aggregate the data in a single account. I recommend using the account used for security operations as a master. You will then need to establish a two-way handshake, inviting member accounts and authorising the master account to be able to consolidate the results.
There are several ways to implement threat detection in AWS but by far the easiest (and perhaps cheapest) set up is to use Amazon’s native GuardDuty. It detects root user logins, policy changes, compromised keys, instances, users and more. As an added benefit, Amazon keep adding new rules as they continue evolving the service.
To detect threats in your AWS environment, GuardDuty ingests CloudTrail, VPC FlowLogs and VPC DNS logs. You don’t need to configure these separately for GuardDuty to be able to access them, simplifying the set up. The price of the service depends on the number of events analysed but it comes with a free 30-day trial which allows you to understand the scope, utility and potential costs.
It’s a regional service, so it should be enabled in all regions, even the ones you currently don’t have any resources. You might start using new regions in the future and, perhaps more importantly, the attackers might do it on your behalf. It doesn’t cost extra in the region with no activity, so there is really no excuse to switch it on everywhere.
To streamline the management, I recommend following the AWS guidance on channelling the findings to a single account, where they can be analysed by the security operations team.
It requires establishing master-member relationship between accounts, where the master account will be the one monitored by the security operations team. You will then need to enable GuardDuty in every member account and accept the invite from the master.
You don’t have to rely on the AWS console to access GuardDuty findings, as they can be streamed using CloudWatch Events and Kinesis to centralise the analysis. You can also write custom rules specific to your environment and mute existing ones customising the implementation. These, however, require a bit more practice, so I will cover them in future blogs.