DLP Selection Process: Defining the Content

In our last post we kicked off the DLP selection process by putting the team together. Once you have them in place, it’s time to figure out which information you want to protect. This is extremely important, as it defines which content analysis techniques you require, which is at the core of DLP functionality. This multistep process starts with figuring out your data priorities and ends with your content analysis requirements: Stack rank your data protection priorities The first step is to list our which major categories of data/content/information you want to protect. While it’s important to be specific enough for planning purposes, it’s okay to stay fairly high-level. Definitions such as “PCI data”, “engineering plans”, and “customer lists” are good. Overly general categories like “corporate sensitive data” and “classified material” are insufficient – too generic, and they cannot be mapped to specific data types. This list must be prioritized; one good way of developing the ranking is to pull the business unit representatives together and force them to sort and agree to the priorities, rather than having someone who isn’t directly responsible (such as IT or security) determine the ranking. Define the data type For each category of content listed in the first step, define the data type, so you can map it to your content analysis requirements: Structured or patterned data is content like credit card numbers, Social Security Numbers, and account numbers – that follows a defined pattern we can test against. Known text is unstructured content, typically found in documents, where we know the source and want to protect that specific information. Examples are engineering plans, source code, corporate financials, and customer lists. Images and binaries are non-text files such as music, video, photos, and compiled application code. Conceptual text is information that doesn’t come from an authoritative source like a document repository but may contain certain keywords, phrases, or language patterns. This is pretty broad but some examples are insider trading, job seeking, and sexual harassment. Match data types to required content analysis techniques Using the flowchart below, determine required content analysis techniques based on data types and other environmental factors, such as the existence of authoritative sources. This chart doesn’t account for every possibility but is a good starting point and should define the high-level requirements for a majority of situations. Determine additional requirements Depending on the content analysis technique there may be additional requirements, such as support for specific database platforms and document management systems. If you are considering database fingerprinting, also determine whether you can work against live data in a production system, or will rely on data extracts (database dumps to reduce performance overhead on the production system). Define rollout phases While we haven’t yet defined formal project phases, you should have an idea early on whether a data protection requirement is immediate or something you can roll out later in the project. One reason for including this is that many DLP projects are initiated based on some sort of breach or compliance deficiency relating to only a single data type. This could lead to selecting a product based only on that requirement, which might entail problematic limitations down the road as you expand your deployment to protect other kinds of content. Share:

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Understanding and Selecting an Enterprise Firewall: Advanced Features, Part 1

Since our main contention in the Understanding and Selecting an Enterprise Firewall series is the movement toward application aware firewalls, it makes sense to dig a bit deeper into the technology that will make this happen and the major uses for these capabilities. With an understanding of what to look for, you should be in a better position to judge whether a vendor’s application awareness capabilities will match your requirements. Application Visibility In the first of our application awareness posts, we talked about visibility as one of the key use cases for application aware firewalls. What exactly does that mean? We’ll break this up into the following buckets: Eye Candy: Most security folks don’t care about fancy charts and graphs, but senior management loves them. What CFO doesn’t turn to jello at the first sign of a colorful pie chart? The ability to see application usage and traffic, and who is consuming bandwidth over a long period over time, provides huge value in understanding normal behavior on your network. Look for granularity and flexibility in these application-oriented visuals. Top 10 lists are a given, but be sure you can slice the data the way you need – or at least export to a tool that can. Having the data is nice; being able to use it is better. Alerting: The trending capabilities of application traffic analysis allows you to set alerts to fire when abnormal behavior appears. Given the infinite attack surface we must protect, any help you can get pinpointing and prioritizing investigative resources increases efficiency. Be sure to have sufficient knobs and dials to set appropriate alerts. You’d like to be able to alert on applications, user/group behavior in specific applications, and possibly even payload in the packets (through regular expression type analysis), and any combination therein. Obviously the more flexibility you have in setting application alerts and tightening thresholds, the better you’ll be able to cut the noise. This sounds very similar to managing an IDS, but we’ll get to that later. Also make sure setting lots of application rules won’t kill performance. Dropped packets are a lousy trade-off for application alerts. One challenge of using a traditional firewall is the interface. Unless the user experience has been rebuilt around an application context (what folks are doing), it still feels like everything is ports and protocols (how they are doing it). Clearly the further you can abstract network behavior to application behavior, the more applicable (and understandable) your rules will be. Application Blocking Visibility is the first step, but you also want to be able to block certain applications, users, and content activities. We told you this was very similar to the IPS concept – the difference is in how detection works. The IDS/IPS uses a negative security model (matching patterns to identify bad stuff) to fire rules, while application aware firewalls use a positive security model – they determine what application traffic is authorized, and block everything else. Extending this IPS discussion a bit, we see most organizations using blocking on only a small minority of the rules/signatures on the box, usually less than 10%. This is for obvious reasons (primarily because blocking legitimate traffic is frowned upon), and gets back to a fundamental tenet of IPS which also applies to application aware firewalls. Just because you can block, doesn’t mean you should. Of course, a positive security model means you are defining what is acceptable and blocking everything else, but be careful here. Most security organizations aren’t in the loop on everything that is happening (we know – quite a shocker), so you may inadvertently stymie a new/updated application because the firewall doesn’t allow it. To be clear, from a security standpoint that’s a great thing. You want to be able to vet each application before it goes live, but politically that might not work out. You’ll need to gauge your own ability to get away with this. Aside from the IPS analogy, there is also a very clear white-listing analogy to blocking application traffic. One of the issues with application white-listing on the endpoints is the challenge of getting applications classified correctly and providing a clear workflow mechanism to deal with exceptions. The same issues apply to application blocking. First you need to ensure the application profiles are accurate and up-to-date. Second, you need a process to allow traffic to be accepted, balancing the need to protect infrastructure and information against responsiveness to business needs. Yeah, this is non-trivial, which is why blocking is done on a fraction of application traffic. Overlap with Existing Web Security Think about the increasing functionality of your operating system or your office suite. Basically, the big behemoth squashed a whole bunch of third party utilities that added value by bundling such capabilities into each new release. The same thing is happening here. If you look at the typical capabilities of your web application filter, there isn’t a lot that can’t be done by an application aware firewall. Visibility? Check. Employee control/management? Check. URL blocking, heuristics, script analysis, AV? Check, check, check, check. The standalone web filter is an endangered species – which, given the complexity of the perimeter, isn’t a bad thing. Simplifying is good. Moreover, a lot of folks are doing web filtering in the cloud now, so the movement from on-premises web filters was under way anyway. Of course, no entrenched device gets replaced overnight, but the long slide towards standalone web filter oblivion has begun. As you look at application aware firewalls, you may be able to displace an existing device (or eliminate the maintenance renewal) to justify the cost of the new gear. Clearly going after the web filtering budget makes sense, and the more expense neutral you can make any purchase, the better. What about web application firewalls? To date, these categories have been separate with less clear overlap. The WAF’s ability to profile and learn about application behavior – in terms of parameter validation, session management, flow analysis, etc. – aren’t available on application aware firewalls. For now. But let’s be clear, it’s not a

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