A bonus episode of The Cloud Pod may be just what the doctor ordered, and this week Justin and Jonathan are here to bring you an interview with Sandy Bird of Sonrai Security. There’s so much going on in the IAM space, and we’re really happy to have an expert in the studio with us this week to talk about some of the security least privilege specifics.

Sonrai (pronounced Son-ree, which means data in Gaelic) was founded in 2017. Sonrai provides Cloud Data Control, and seeks to deliver a complete risk model of all identity and data relationships, which includes activity and movement across cloud accounts, providers, and third party data stores.

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Sandy is the co-founder and CTO of Sonrai, and has a long career in the tech industry. He was the CTO and co-founder of Q1 Labs, which was acquired by IBM in 2011, and helped to drive IBM security growth as CTO for global business security there.

One of the big questions we start the interview with is just how has IAM evolved – and what kind of effect have those changes had on the identity models? Enterprise wants things to be least privilege, but it’s hard to find the logs. In cloud, however *most* things are logged – and so least privilege became an option.

Sonrai offers the first cloud permissions firewall, which enables one click least privilege management, which is important in the current environment where the platforms operate so differently from each other. With this solution, you have better control of your cloud access, limit your permissions, attack surface, and automate least privilege – all without slowing down DevOps2.

Is the perfect policy achievable? Sandy breaks it between human identities and workload identities; they’re definitely separate. He claims, in workload identities the perfect policy is probably possible. Human identity is hugely sporadic, however, it’s important to at least try to get to that perfect policy, especially when dealing with sensitive information. One of the more interesting data pieces they found was that less than 10% of identities with sensitive permissions actually used them – and you can use the information to balance out actually handing out permissions versus a one time use case.

Sonrai spent a lot of time looking at new solutions to problems with permissions; part of this includes purpose-built integration, offering a flexible open GraphQL API with prebuilt integrations.

Sonrai also offers continuous monitoring; providing ongoing intelligence on all the permission usage – including excess permissions – and enables the removal of unused permissions without any sort of disruptions. Policy automation automatically writes IAM policies tailored to access needs, and simplifies processes for teams.

On demand access is another tool that gives on demand requests for permissions that are restricted with a quick and efficient process.

Sandy: “The unbelievably powerful model in AWS can do amazing things, especially when you get into some of the advanced conditions – but man, for a human to understand what all this stuff is, is super hard. Then you go to the Azure model, which is very different. It’s an allow first model. If you have an allow anywhere in the tree, you can do whatever is asked, but there’s this hierarchy to the whole thing, and so when you think you want to remove something you may not even be removing it., because something above may have that permission anyway. It’s a whole different model to learn there.”

Sandy: “Only like 8% of those identities actually use the sensitive parts of them; the other 92 just sit in the cloud, never being used, and so most likely during that break loss scenario in the middle of the night, somebody’s troubleshooting, they have to create some stuff, and overpermission it . If we control this centrally, the sprawl doesn’t happen.”

Sandy: There is this fear that if I remove this identity, I may not be able to put it back the way it was if it was supposed to be important… We came up with a secondary concept for the things that you were worried about… where we basically short circuit them, and say these things can’t log in and be used anymore, however we don’t delete the key material, we don’t delete the permissions. We leave those all intact.”

In this episode, Andrew Krug talks about Datadog as a security observability tool, shedding light on some of its applications as well as its benefits to engineers.

Andrew is the lead in Datadog Security Advocacy and Datadog Security Labs. Also a Cloud Security consultant, he started the Threat Response Project, a toolkit for Amazon Web Services first responders. Andrew has also spoken at Black Hat USA, DEFCON, re:Invent, and other platforms..

Datadog is focused on bringing security to engineering teams, not just security people. One of the biggest advantages of Datadog or other vendors is how they ingest and normalize various log sources. It can be very challenging to maintain a reasonable data structure for logs ingested from cloud providers.

Vendors try to provide customers with enough signals that they feel they are getting value while trying not to flood them with unactionable alerts. Also, considering the cloud friendliness for the stack is crucial for clients evaluating a new product.

Datadog is active in the open-source community and gives back to groups like the Cloud native computing foundation. One of their popular open-source security tools created is Stratus-red-team which simulates the techniques of attackers in a clean room environment. The criticality of findings is becoming a major topic. It is necessary when evaluating that criticality is based on how much risk applies to the business, and what can be done.

One of the things that teams struggle with as high maturity DevOps is trying to automate incident handling or response to critical alerts as this can cause Configuration Drift which is why there is a lot of hesitation to fully automate things. Having someone to make hard choices is at the heart of incident handling processes.

Datadog Cloud SIEM was created to help customers who were already customers of logs. Datadog SIEM is also very easy to use such that without being a security expert, the UI is simple. It is quite difficult to deploy a SIEM on completely unstructured logs, hence being able to extract and normalize data to a set of security attributes is highly beneficial. Interestingly, the typical boring hygienic issues that are easy to detect still cause major problems for very large companies. This is where posture management comes in to address issues on time and prevent large breaches.

Generally, Datadog is inclined towards moving these detections closer to the data that they are securing, and examining the application run time in real-time to verify that there are no issues. Datadog would be helpful to solve IAM challenges through CSPM which evaluates policies. For engineering teams, the benefit is seen in how information surfaces in areas where they normally look, especially with Datadog Security products where Issues are sorted in order of importance.

Security Observability Day is coming up on the 18th of April when Datadog products will be highlighted; the link to sign up is available on the Datadog Twitter page and Datadog community Slack. To find out more, reach out to Andrew on Twitter @andrewkrug and on the Datadog Security Labs website.

Top Quotes

• “I think that great security solutions…start with alerts that you are hundred percent confident as a customer that you would act on”
• “When we talk about the context of ‘how critical is an alert?’ It is always nice to put that risk lens on it for the business”
• “Humans are awesome unless you want really consistent results, and that’s where automating part of the process comes into play”
• “More standardization always lends itself to better detection”

In this episode, Ravi Mayuram highlights the functionality of Couchbase as an evolutionary database platform, citing several simple day-to-day use cases and particular advantages of Couchbase.

Ravi Mayuram is CTO of Couchbase. He is an accomplished engineering executive with a passion for creating and delivering game-changing products for startups as well as Fortune-500 industry-leading companies.

Notes

Couchbase set out to build a next-generation database. Data has evolved greatly with IT advancements. The goal was to build a database that will connect people to the newer technologies, addressing problems that relational systems did not have to solve. The fundamental shift is that earlier systems were internally focused, built for trained users but now the systems are built directly for consumers. This shift also plays out in the vast difference in the number of consumers now interacting with these systems compared to the fewer trained users previously interacting with the systems.

One of the key factors that sets Couchbase apart is the No-SQL Database. It is a database that has evolved by combining five systems; a Cache and Key-value store, a Document store, a Relational document store, a Search system, and an Analytical system. Secondly, Couchbase performs well in the geo-distributed manner such that with one click, data is made available across availability zones. Lastly, all of this can be done at a large scale in seconds.

Regarding the global database concept that Google talks about, a globally consistent database may not be needed by most companies. The performance will be the biggest problem as transaction speed will be considerably low. Couchbase does these transactions locally within the data center and replicates them on the other side. The main issue of relational systems is that they make you pay the price of every transaction no matter how minor, but with Couchbase, it is possible to pay only the cost only with certain crucial transactions.

Edge has become a part of the enterprise architecture even such that people now have edge-based solutions. Two edges are emerging; the Network edge and the Tool edge where people are interfacing. Couchbase has built a mobile database available on devices, with sync capability.

As a consumer, the primary advantage of bringing data closer to the consumer is the latency issue. Often, data has to go through firewalls and multiple steps which delays it but this is the benefit of Couchbase. The user simply continues to have access to the data while Couchbase synchronizes the data in the back.

One of the applications of Couchbase in healthcare is insulin tracking. With many devices that monitor insulin which must work everywhere you go, Couchbase Lite does the insulin tracking, keeps the data even in the absence of a network, and later syncs it for review by healthcare professionals. This is also useful in operating rooms where the network is not accessible. The real benefit is seen when the data eventually gets back to the server and can be interpreted to make decisions on patient care.

The Couchbase Capella Service runs in the cloud and allows clients to specify what data should be sent to the edge and what should not be. This offers privacy and security measures, such that even in the loss or damage of a device, the data is secure and can be recovered. To effectively manage edge in devices, a lot of problems must be addressed to make it easier.

One of the concerns for anyone coming into Couchbase Capella is the expense of data extraction from the cloud, however, Couchbase is available on all three cloud providers. Also, with Couchbase, there is no need to keep replicating data as you can work on the data without moving it, which largely saves costs.

Other use cases for Couchbase include information for flight bookings, flight crew management systems, hotel reservations, and credit card payments. To learn more, visit the Couchbase website. There is also a free trial for the Couchbase Capella Service.  

Top Quotes

• “The modern database has to do more than what the old database did”
• “Managing edge in devices is not an easy thing, and so you have to solve a lot of problems so it becomes easier”