Most encryption implementations are protecting against the wrong threat. These methods do nothing to
counter modern day data breach risk and attacks.
Common Misconceptions With Encryption
Across the industry from security professionals to auditors, there are some common misconceptions about encryption methods that a lot people get confused about in terms of the threat model and risks that are actually being mitigated.
Without understanding the methods and what you are protecting against, it’s difficult to ensure the appropriate data protection model. Further, actually implementing encryption can be quite complex with several interdependencies.
Read our article on the threat model and how these gaps do not stop attackers in “Why you can’t stop data breaches – Part I”
Read the article by our CTO, PD Kolte, on “Why data tokenization is insecure”
Download the white paper on “Simplifying Application Level Encryption”
Below is an overview of some of the different methods that are commonly available.
Disk Level Encryption
Transparent Data Encryption (TDE)
Field Level Encryption
Record Level Encryption
Encrypts data at the physical disk layer and protects against physical data theft of a hard drive or laptop. Operating system access provides full access to the data in the clear.
Provides encryption of a database as a container on the file system. This protects against physical data theft and against access from a non-DBA who may have access to a system where the database resides. Access to the database provides full access to the data in the clear.
Encrypts columns or fields of data in a structured data environment. This protects against privileged users and DBAs with access to a database, insider threat, 3rd party developers, and attackers moving laterally (east-west) in an environment from seeing sensitive data in the clear. This method is sometimes referred to as Application Level Encryption (ALE)
Encrypts data on a per row or record basis in a structured data environment using different keys for different rows. This helps prevent oversharing of data in co-mingled data stores or multi-tenant SaaS environments and can also be applied to segment data based on classification. This method is sometimes referred to as Record Level Encryption (RLE)
All of these methods are commonly referred to as Encryption At-Rest. Perhaps part of the issue is with the term “Encryption At-Rest”, because technically, all of these methods are at-rest encryption options. But, clearly the risk mitigation provided is different based on the data protection method.
On the right is an example of Transparent Data Encryption (TDE).
As you can see, anyone with access to the database sees the data in the clear.
- It does nothing to protect against a modern day hack or breach. (most recent breaches had TDE in place and data was still stolen)
- Data in the logs are in the clear, which violates compliance regulations such as PCI
- Data in memory is in the clear
- Attackers moving laterally in the network gain access to data in the clear
To the left is an example of Application Level Encryption or Field Level Encryption.
- Privileged users and insiders with access to the system see the data encrypted
- Attackers accessing the system laterally through the network see encrypted data
- Data in logs are encrypted
- Data in memory are encrypted
Baffle delivers a transparent data protection service layer that secures data at the field or file level via a "no code" model. The solution supports tokenization, format preserving encryption (FPE), database and file AES-256 encryption, privacy preserving analytics and access control. As a transparent solution, cloud native services are easily supported with almost no performance impact.
No application code modification
Virtually no performance
Integrates easily into your
AES encryption in memory, in use,
It was, literally and figuratively, the perfect storm. A blizzard forced everyone from a Wall Street wealth management firm to work from home. At the same time, clients were denied access to their information and called their contacts at the firm understandably concerned. The operations team determined that with the data analysts working from home,…
Organizations have flocked from on-premises to the cloud over the past year, and protecting data during the transition has proven to be a monumental task. But now companies must focus on what happens after the migration. The new reality is that these organizations and their cloud providers work under a shared responsibility model, in which…
This blog looks at vault-based data tokenization methods and some key challenges on using such approaches in modern compute environments. Data tokenization is a method that transforms a data value to a token value and is often used for data security and compliance. There are basically three approaches to enabling data tokenization: Vault-based Tokenization Vaultless…