Beyond the Firewall – Data Loss Prevention

Thursday, April 05, 2012

Danny Lieberman

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What a simple idea. It doesn’t matter how they break into your network or servers – if attackers can’t take out your data, then you’ve mitigated the threat.

Data loss prevention is a category of information security products that has matured from Web / email content filtering products into technologies that can detect unauthorized network transfer of valuable digital assets such as credit cards.

This paper reviews the motivation for and the taxonomies of advanced content flow monitoring technologies that are being used to audit network activity and protect data inside the network.

Motivation – why prevent data loss?

The majority of hacker attacks and data loss events are not on the IT infrastructure but on the data itself. If you have valuable data (credit cards, customer lists, ePHI) then you have to protect it.

Content monitoring has traditionally meant monitoring of employee or student surfing and filtering out objective content such as violence, pxxnography and drugs. This sort of Web content filtering became “mainstream” with wide-scale deployments in schools and larger businesses by commercial closed source companies such as McAfee and Bluecoat and Open Source products such as Censor Net and Spam Assassin. Similar signature-based technologies are also used to perform intrusion detection and prevention.

However, starting in 2003, a new class of content monitoring products started emerging that is aimed squarely at protecting firms from unauthorized “information leakage”, “data theft” or “data loss” no matter what kind of attack was mounted.

Whether the data was stolen by hackers, leaked by malicious insiders or disclosed via a Web application vulnerability, the data is flowing out of the organization. The attack vector in a data loss event is immaterial if we focus on preventing the data loss itself.

The motivation for using data loss prevention products is economic not behavioral; transfer of digital assets  such as credit cards and PHI by trusted insiders or trusted systems can cause much more economic damage than viruses to a business.

Unlike viruses, once a competitor steals data you cannot reformat the hard disk and restore from backup.

Companies often hesitate from publicly reporting data loss events because it damages their corporate brand, gives competitors an advantage and undermines customer trust no matter how much economic damage was actually done.

Who buys DLP (data loss prevention)?

This is an interesting question. On one hand, we understand that protecting intellectual property, commercial assets and compliance-regulated data like ePHI and credit cards is  essentially an issue of  business risk management. On the other hand, companies like Symantec and McAfee and IBM sell security products to IT and information security managers.

IT managers focus on maintaining predictable execution of business processes not dealing with unpredictable, rare, high-impact events like data loss.  Information security managers find DLP technology interesting (and even titillating – since it detects details of employee behavior, good and bad) but an information security manager who buys Data loss prevention (DLP) technologies essentially admitting that his perimeter security (firewall, IPS) and policies and procedures are inadequate.

While data loss prevention may be a problematic sale for IT and information security staffers, it plays well into the overall risk analysis,  risk management and compliance processes of the business unit.

Data loss prevention for senior executives

There seem to be three schools of thought on this with senior executives:

  1. One common approach is to ignore the problem and brush it under the compliance carpet using a line of reasoning that says “If I’m PCI DSS/HIPAA compliant, then I’ve done what needs to be done, and there is no point spending more money on fancy security technologies that will expose even more vulnerabilities”.
  2. A second approach is to perform passive data loss detection and monitor flow of data(like email and file transfers) without notifying employees or the whole world. Anomalous detection events can then be used to improve business processes and mitigate system vulnerabilities. The advantage of passive monitoring is that neither employees nor hackers can detect a Layer 2 sniffer device and a sniffer is immune to configuration and operational problems in the network. If it can’t be detected on the network. then this school of thought has plausible deniability.
  3. A third approach takes data loss prevention a step beyond security and turns it into a competitive advantage. A smart CEO can use data loss prevention system as a deterrent and as a way of enhancing the brand (“your credit cards are safer with us because even if the Saudi hacker gets past our firewall and into the network, he won’t be able to take the data out”).

A firewall is not enough

Many firms now realize that a firewall is not enough to protect digital assets inside the network and look towards incoming/outgoing content monitoring. This is because: 

  1. The firewall might not be properly configured to stop all the suspicious traffic.

  2. The firewall doesn’t have the capability to detect all types of content, especially embedded content in tunneled protocols.

  3. The major of hacker attacks and data loss events are not on the IT infrastructure but on the data itself.

  4. Most hackers do not expect creative defenses so they assume that once they are in, nobody is watching their nasty activities.

  5. The firewall itself can be compromised. As we have more and more Day-0 attacks and trusted insider threats, so it is good practice to add additional independent controls.

Detection

Sophisticated incoming and outgoing (data loss prevention or DLP) content monitoring technologies basically use three paradigms for detecting security events

  1. AD- Anomaly Detection – describes normal network behavior and flags everything else
  2. MD- Misuse Detection – describes attacks and flags them directly
  3. BA – Burglar alarm – describes abnormal network behavior (“detection by exception”)

In anomaly detection, new traffic that doesn’t match the model is labeled as suspicious or bad and an alert is generated. The main limitation of anomaly detection is that if it is too conservative, then it will generate too many false positives (a false alarm) and over time the analyst will ignore it.

On the other hand, if a tool rapidly adapts the model to evolving traffic change, then too little alerts will be generated and the analyst will again ignore it.

Misuse detection describes attacks and flags them directly, using a database of known attack signatures and constantly tries to match the actual traffic against the database. If there is a match, an alert is generated. The database typically contains rules for:

  1. Protocol Stack Verification – RFC’s, ping of death, stealth scanning etc.
  2. Application Protocol Verification – WinNuke , invalid packets that cause DNS cache corruption etc.
  3. Application Misuse – misuse that causes applications to crash or enables a user to gain super user privileges; typically due to buffer overflows or due to implementation bugs.
  4. Intruder detection. Known attacks can be recognized by the effects caused by the attack itself. For example, Back Orifice 2000 sends traffic on default port is 31337
  5. Data loss detection – for example by file types, compound regular expressions, linguistic and/or statistical content profiling. Data loss prevention or detection needs to work at a much higher level than intrusion detection – since it needs to understand file formats and analyze the actual content such as Microsoft Office attachments in a Web mail session as opposed to doing simple pattern matching of an http request string.

Using a burglar alarm model, the analyst needs deep understanding of the network and what should not happen with it. He builds rules that model how the monitored network should conceptually work, in order to generate alerts when suspicious traffic is detected. The richer the rules database, the more effective the tool.

The advantage of the burglar alarm model is that a good network administrator can leverage his knowledge of servers, segments and clients (for example a Siebel CRM server which is a client to a Oracle database server) in order to focus-in and manage-by-exception.

What about prevention?

Anomaly detection is an excellent way of identifying network vulnerabilities but a customer cannot prevent extrusion events based on general network anomalies such as usage of anonymous ftp. In comparison there is a conceptual problem with misuse detection.

If misuse is detected then unless the event can be prevented (either internally with a TCP reset, by notifying the router or firewall) – then the usefulness of the devices is limited to forensics collection.

What about security management?

SIM – or security information management consolidates reporting, analysis, event management and log analysis. There are a number of tools in this category – Netforensics is one.

SIM systems do not perform detection or prevention functions – they manage and receive reports from other systems. Checkpoint for example is a vendor that provides this functionality with partnerships.

Summary

There are many novel DLP/data loss prevention products, most provide capabilities far ahead of both business and IT infrastructure management that are only now beginning to look towards content monitoring behind the firewall.

DLP (Data loss prevention) solutions join an array of content and application-security products around the traditional firewall. Customers are already implementing a multitude of network security products for Inbound Web filtering, Anti-virus, Inbound mail filtering and Instant Messaging enforcement along with products for SIM and integrated log analysis.

The industry has reached the point where the need to simplify and reduce IT security implementation and operational costs becomes a major purchasing driver, perhaps more dominant than any single best-of-breed product.

Perhaps data loss prevention needs to become a network security function that is part of the network switching fabric; providing unified network channel and content security.

Software Associates helps healthcare customers design and implement such a unified network channel and enterprise content security solution today enabling customers to easily define policies such as “No Instant Messaging on our network” or “Prevent patient data leaving the company over any channel that is not an authorized SSH client/server”.

Cross-posted from Israeli Software

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