WEBVTT

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When considering the implementation of log capture and analysis,

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would you be shocked to know that there is a standard for it?

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Probably not.

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If you were to look NIST Special Publications 800‑92,

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Guide to Computer Security Log Management,

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it focuses on the log being a record of events that have occurred within an

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organization's systems and the organization's network.

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And when you think of what is composed of the things in the log,

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there could be many sources. It could be filled with events that are

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completely innocuous. Elements of log collection are focusing on what is

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regulatory or a compliant requirement that we have.

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What is the non‑repudiation? If we have looked at the authentication,

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the authorization, and the identification of an entity,

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how do we know that that entity is actually doing what it's

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supposed to be doing? The performance is also a good key reason

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in our motivation for log collection.

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This is performance both of the cloud service provider's platform

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and anything that we have created on top of it.

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We need to be aware of the separation between real‑time alerts

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and the recording of things inside of logs.

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So the real‑time alerts can be something that we put on top of

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the logging event correlation that says that this is a matter

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that needs higher escalation.

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Why?

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Because that event could actually be part of an incident that deserves

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an investigation. A little bit later, we'll look through a

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demonstration of creating something that has the full lifecycle

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management of the monitoring, the collection,

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the real‑time alerts, and even event correlation. When it comes to

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log management in the log management process,

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we first have to define the logging requirements and the goals.

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What is it that the organization wants to accomplish?

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That means that there has to be prioritization of log

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management appropriate to the organization's risk

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tolerance and risk management behaviors.

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The idea is to reduce risk. Policies need to be developed that

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clearly define the mandatory requirements and suggested

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recommendations for log management activities.

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It includes log generation, the transmission,

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the storage, the analysis, and the disposal of the logs.

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There needs to be a step‑by‑step process for collecting logs needed as evidence.

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Organizations may wish to acquire copies of original log files,

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centralized log files, and to interpret the log data.

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This may mean additional conversations with the cloud service

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provider to have access to resources that may not be part of what

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is naturally supplied inside of the SLA.

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Additional factors that will influence the log design could be the cost and the

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volume of the data that's going to be processed or stored.

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Typically, with cloud service providers,

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it's not the management of the log in their platform that has cost, but it's

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typically the storage of it and also the movement of that log outside of the

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platform that tends to exponentiate the cost.

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You also should be concerned about the disposition of the logs,

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whether it's in transit or at rest so that it's secure. The information

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that's coming from these logs could actually be quite detrimental if

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exposed to eyes that should not be seeing it.

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The security concerns for logs have to do with what the logging status is.

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When we talk about logging status,

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we're saying there should be monitoring of the log sources

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and monitoring of the logs themselves.

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What kind of archival process do the logs go through?

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Is there some kind of overriding that takes place at

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the end of a particular period?

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How about checking for upgrades and patches to logging software and

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acquiring the testing and deploying them in a correct way?

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Logs also need to be concerned about synchronizing time,

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ensuring that each log's host clock is synchronized to a

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common time source. Also, reconfiguring logging as needed

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based upon policy changes,

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technology changes, and other factors that may take place. And then

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the operational existence of the log so that we can detect anomalies

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that may occur that need to be addressed.

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The SIEM system is the centralized collection and monitoring of

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security and event logs from different systems.

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The SIEM system allows for the correlation of different events and

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early detection of attacks. At the most basic level, a SIEM system can

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be rules based or employ a statistical correlation engine to establish

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relationships between event logs.

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So, what are some common characteristics? That you're pulling in raw

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information from various system logs. Going through a process of aggregating

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those many different systems into a single repository.

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You're normalizing that information so that it conforms to a particular

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format, and the comparisons can be more meaningful.

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You're doing the analyzing of the capabilities. You're analyzing and using

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analyzation tools in order to map and extract target information.

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And then, you also may be doing some type of alerting as well.

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Different SIEM characteristics and benefits.

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If you have a locally hosted SIEM,

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it offers easy access and lowered risk of external disclosure.

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You can have externally hosted, which may prevent an attacker from tampering

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with the data based upon the expertise of the one hosting it.

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It could be a hybrid because you're consuming

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services both locally and in the cloud.

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It could be done from an external storage perspective to support policy so that

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the administrators locally do not have access to the log.

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It could be part of your disaster recovery in that it is taking

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place based upon transactions, and it is creating a transaction

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log that you can recover based upon.

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And like we said, with privilege users,

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we want to make sure that we're screening out and preventing the capability

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for those privilege users from managing their own logs.

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Next generation capabilities and log management says that the SIEM

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system may be based upon a modern data lake technology like Amazon's

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S2 or Hadoop or Elasticsearch. Having large amounts of low‑cost

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space can afford greater retention when it comes to doing this on a

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large‑scale basis.

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You also may be using other types of analytics,

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like user and entity behavior,

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which utilizes machine learning and behavioral profiling for deeper

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intelligence in identifying threats that may not seem like an anomaly

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from a log that is looking just at two dimensions.

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There is growing usefulness in this world as we connect the SIEM

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along with other security orchestration and automation technology

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that assist in identifying and responding to security incidents

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that are inside the SOC.

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Large amounts of telemetry data also supports the continuous

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improvement of a zero trust architecture.
