Dark Reading is part of the Informa Tech Division of Informa PLC

This site is operated by a business or businesses owned by Informa PLC and all copyright resides with them.Informa PLC's registered office is 5 Howick Place, London SW1P 1WG. Registered in England and Wales. Number 8860726.

Endpoint

3/14/2018
02:00 PM
John Moynihan
John Moynihan
Commentary
Connect Directly
Twitter
LinkedIn
RSS
E-Mail vvv
50%
50%

Segmentation: The Neglected (Yet Essential) Control

Failure to deploy measures to contain unauthorized intruders is a recipe for digital disaster.

Throughout the past decade, the information security profession has pursued an approach centered around protecting the network perimeter. While this proactive strategy has become the foundation of most enterprise programs, organizations must be equally capable of reacting to attacks by containing intruders once they have entered the network.

Although identifying and mitigating perimeter threats is essential, the fact remains that cybercriminals and nation-state actors are, with alarming frequency, defeating the most-hardened networks. Therefore, it is imperative that practitioners acknowledge this dynamic and deploy containment measures to isolate intrusions.

Network segmentation is a critical weapon in the escalating battle to protect against unauthorized access.

Segmentation
The vast majority of cyberattacks originate within the most vulnerable area of an organization, the user environment. Once the initial compromise occurs through the use of stolen credentials or the successful delivery of a social engineering overture, those launching the attack enjoy a foothold from which to search for critical platforms and sensitive data. By dividing a network into segments and restricting lateral access, security teams are capable of containing intruders and dramatically reducing the entity's attack surface.

When intruders enter an inadequately segmented, or "flat," network, they often enjoy unbridled movement and will eventually gain access to payment applications, sensitive databases, and critical infrastructure systems. Through segmentation, these critical technologies may be isolated and thereby protected.

Although many high-profile and destructive cyber campaigns have been attributed to poor segmentation, perhaps the most widely reported incident was the 2013 Target breach. According to multiple sources, a group based in Eastern Europe breached the retailer's perimeter by first stealing credentials from one of the company's service providers, an HVAC vendor. 

Once inside the system that monitors Target's heating and air conditioning functions, the cybercriminals were able to proceed, without detection, to the point-of-sale environment. Ultimately, malware was installed on approximately 36,000 payment terminals, allowing for the hackers to steal 40 million credit and debit card numbers from unsuspecting shoppers.

This scenario is the digital equivalent of a bank robber entering the lobby of a major financial institution and proceeding, unimpeded and without being noticed, to a vault containing its customers most-valuable items.

The Path Forward
Management has long avoided the deployment of comprehensive network segmentation due to a variety resource and operability concerns. Recently, however, dramatic advances in enterprise software solutions provide practitioners with scalable, customized options to address this issue.

Prior to implementing a segmentation strategy, a clear consensus must be reached regarding an organization's sensitive data and critical platforms. Given that isolating these environments will be the ultimate goal, there can be no ambiguity on this issue. Also, it is necessary to map the existing communication paths and application dependencies within the network. Once these preliminary tasks have been completed, the following segmentation options may be considered:

Environmental: Isolates the most vulnerable environments, such as user and development, from the rest of the network and prevents intruders from breaching the low-hanging fruit and then moving laterally. This is known as the "coarsest" form of segmentation and should exist in every organization.

Application: Ensures that certain high-value applications are insulated from all others and provides an additional roadblock for attackers attempting to travel across applications.

Process: The "finest-grain" form of segmentation ensures that only active communications channels may be used and prevents any use of dormant paths.    

The rapidly evolving nature of the cyber-threat landscape requires adaptive information security professionals. Although preventing intrusions is, and should remain, a primary goal, failure to deploy measures to contain unauthorized intruders is a recipe for digital disaster.

Related Content:

Interop ITX 2018

Join Dark Reading LIVE for two cybersecurity summits at #InteropITX. Check out the security track agenda here, then register! Early Bird Rates expire Friday March 16. Use Promo Code DR200 & save $200. 

John Moynihan, CGEIT, CRISC, is President of Minuteman Governance, a Massachusetts cybersecurity consultancy that provides services to public and private sector clients throughout the United States. Prior to founding this firm, he was CISO at the Massachusetts Department of ... View Full Bio
 

Recommended Reading:

Comment  | 
Print  | 
More Insights
Comments
Threaded  |  Newest First  |  Oldest First
dstory752
100%
0%
dstory752,
User Rank: Apprentice
3/16/2018 | 11:50:25 AM
Segmentation vs Convergence
Segmentation becomes a 'hard sell' when many organizations are hell-bent on network convergence, at any cost.  The cost is usually paid for in the form of a data breach.
COVID-19: Latest Security News & Commentary
Dark Reading Staff 9/25/2020
Hacking Yourself: Marie Moe and Pacemaker Security
Gary McGraw Ph.D., Co-founder Berryville Institute of Machine Learning,  9/21/2020
Startup Aims to Map and Track All the IT and Security Things
Kelly Jackson Higgins, Executive Editor at Dark Reading,  9/22/2020
Register for Dark Reading Newsletters
White Papers
Video
Cartoon
Current Issue
Special Report: Computing's New Normal
This special report examines how IT security organizations have adapted to the "new normal" of computing and what the long-term effects will be. Read it and get a unique set of perspectives on issues ranging from new threats & vulnerabilities as a result of remote working to how enterprise security strategy will be affected long term.
Flash Poll
How IT Security Organizations are Attacking the Cybersecurity Problem
How IT Security Organizations are Attacking the Cybersecurity Problem
The COVID-19 pandemic turned the world -- and enterprise computing -- on end. Here's a look at how cybersecurity teams are retrenching their defense strategies, rebuilding their teams, and selecting new technologies to stop the oncoming rise of online attacks.
Twitter Feed
Dark Reading - Bug Report
Bug Report
Enterprise Vulnerabilities
From DHS/US-CERT's National Vulnerability Database
CVE-2020-15208
PUBLISHED: 2020-09-25
In tensorflow-lite before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, when determining the common dimension size of two tensors, TFLite uses a `DCHECK` which is no-op outside of debug compilation modes. Since the function always returns the dimension of the first tensor, malicious attackers can ...
CVE-2020-15209
PUBLISHED: 2020-09-25
In tensorflow-lite before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, a crafted TFLite model can force a node to have as input a tensor backed by a `nullptr` buffer. This can be achieved by changing a buffer index in the flatbuffer serialization to convert a read-only tensor to a read-write one....
CVE-2020-15210
PUBLISHED: 2020-09-25
In tensorflow-lite before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, if a TFLite saved model uses the same tensor as both input and output of an operator, then, depending on the operator, we can observe a segmentation fault or just memory corruption. We have patched the issue in d58c96946b and ...
CVE-2020-15211
PUBLISHED: 2020-09-25
In TensorFlow Lite before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, saved models in the flatbuffer format use a double indexing scheme: a model has a set of subgraphs, each subgraph has a set of operators and each operator has a set of input/output tensors. The flatbuffer format uses indices f...
CVE-2020-15212
PUBLISHED: 2020-09-25
In TensorFlow Lite before versions 2.2.1 and 2.3.1, models using segment sum can trigger writes outside of bounds of heap allocated buffers by inserting negative elements in the segment ids tensor. Users having access to `segment_ids_data` can alter `output_index` and then write to outside of `outpu...