Log management is an essential ingredient in the administration of enterprise technology infrastructure and data. However, not just any form of log management will do. To ensure your log management strategy and tactics are effective, you must invest significant thought, time and effort to make it work.
By applying the following tips in conjunction with Papertrail’s best practices, you can increase the odds of success for your log management.
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Creating a requirement specification document from scratch can be a time-consuming process. However, if you do not get the requirement specification right, you’ll have difficulty finding the most suitable log management system. Log management is used to get business insight until Big data was introduced. With Big data maintaining log files and analyzing logs has become easy.
The good thing about an exhaustive requirement specification is that you can reuse it (with minor modification) if you ever need to procure a new log management tool. The core components of the document such as log file format and the data to be captured in the log file will remain the same.
The average enterprise’s technology infrastructure generates millions of log events every day. If you have to monitor all this log data, you will not only rapidly exhaust the storage space on your log servers but you will also be adding unnecessary complexity to your log analysis. You, therefore, have to make a decision on what type of events your log management tool will focus on. Artificial intelligence plays a vital role in this, it can efficiently turn the monitoring process into a real-time monitoring system.
All system events are not of equal significance or value. Any type of log data that is critical for auditing and compliance purposes must be logged. So should any information that streamlines and speeds up your ability to troubleshoot system problems, solve end-user issues or keep track of security incidents. On the other hand, it isn’t necessary for you to monitor test environment data. You should also avoid logging sensitive information like credit card numbers.
In order to develop effective correlation rules, log management systems must have sufficient volume and variety of contextual data to analyze.
For example, what was the origin of a given event? To know this, one would need to have considerable knowledge of the originating IP address. Ergo, the log management system must be keeping a record of that information if the engine is to parse it. If an organization wants to write log analysis alerts and rules for a specific activity, the log data must accurately capture such activity.
Log storage would seem like a relatively straightforward aspect of log management. However, this is one area where you can easily drop the ball with catastrophic results. It’s especially risky to plan log storage based on average log volumes. Instead, storage should take into account high load peaks.
When systems are running smoothly, the volume of log data generated is predictable and near-uniform from day to day with the only variable being the reasonable fluctuation in transaction volume and system utilization. However, when the system runs into critical errors, you can expect a surge in log volume. The danger here is that if your storage hits its limit unexpectedly, you will lose the latest logs which now cannot be stored and yet are fundamental to the resolution of the errors.
The last thing, an organization need is to be inundated by new spreadsheets or lists filled with rows of data but without any overarching analytical strategy to facilitate making sense of it all. Log alerts should not just be configured based on the static characteristic of an individual row of data but more importantly, based on a clearly defined baseline of acceptable or expected activity.
For example, say one of the baseline requirements for a system alert is two consecutive failed logins. If password rules are modified from accepting simple dictionary words to only allowing 8+ character strings of non-dictionary alphanumeric characters and symbols, you can expect a temporary rise in failed logins as users adapt to the new rules.
An intelligent log management system could monitor trends and generate reports for system administrator consumption and action including, in our example, triggering a temporary alteration to the failed login alert threshold.
During a crisis, IT staff may go into a panic and adopt a reactive approach to problem resolution. But relying on speculation, intuition and unrelated information to get to the root of the problem is a waste of precious time, money and resources. Instead, the incident investigation should be driven by the relevant hard data found in system logs.
Logs are a record of exactly what has happened. Often, this is all that you need to determine the cause of the outage. By basing your response on the facts of an incident, you stand a better shot at understanding the problem and resolving it conclusively.
Irrespective of how sophisticated the features of a log management tool might be, it won’t have much impact if it is not configured right. Procuring a log management tool and then simply throwing it at your logging problems will not work. You must be prepared to install, configure and manage the tool meticulously.
A log management system must be configured to parse the data and events that matter to the business. Both alerts and the alert console must lend itself to the smooth communication of critical security events. That way, the reports generated will have tangible technical and financial value for the organization. If you commit to logging management technology, you must also commit to making sure it works well.
Logs contain a wide range of sensitive information such as customer personal data and API internal access keys. If such data falls into the wrong hands, it could be used for fraud, identity theft or unauthorized access. Hackers will also often attempt to access and alter log files in order to hide their tracks. Log security isn’t just something you should do but it may also be required by regulations such as the EU’s GDPR.
To keep sensitive log information safe, develop a log security policy and procedure that requires and details how to log data should be encrypted or anonymized before it’s shipped to a third party. Limit who can access the log servers. For clear accountability, ensure each user ID that can access the log management tool and log server is assigned to a specific individual—there should be no shared logins.
The security policy must also address log data retention times. Log retention times will vary depending on the application and the event type. Some application log data necessary for troubleshooting may be useful for just a couple of days. Business transaction and security related logs call demand longer retention windows.
Machine learning is the ultimate solution to analyze large volumes of data with a higher accuracy level. For classified data, log analysis uses supervised machine learning. It helps in clustering the unstructured data into meaningful groups.
Log management shouldn’t be viewed as a routine afterthought but rather as an essential component in the management of technology infrastructure. By applying the above tips, you can make your log management effort and investment count.
