Page 115 - Cyber Defense eMagazine September 2022

Integrating Zero Trust at the Data Level

The traditional network-centric approach to zero trust does not address these issues. But what if we
implemented zero trust, along with attribute-based access controls (ABAC), at the data level instead?
What would this look like?

All data would have security labels applied on write, i.e., immediately protected at ingestion. The system
should be able to handle all data types – structured or unstructured, streaming or static – in their raw
forms, retaining the data’s original structure to ensure greater flexibility and scalability.

Attribute-based access control allows resources to be protected by a policy that takes users’ attributes
and credentials into account, not just their roles, and can allow for more complex rules. And if ABAC is
used to protect data at a fine-grained level, it ensures that data segregation is no longer necessary. Unlike
the more common role-based access control (RBAC), which uses course-grained roles and privileges to
manage access, ABAC is considered the next generation of access control because it’s “dynamic,
context-aware and risk-intelligent.” These access controls can be applied at the level of the dataset,
column, attribute-based row, document/file, and even individual paragraphs. In this scenario, people see
only the data they need (and are authorized) to see, even if they’re looking at the same file.

Let’s look at our earlier examples through the lens of zero trust for data. A data analyst could upload
sensitive information that would be immediately labeled. Even the database administrator would not be
able to view this information – they can manage the system resources, but not view the confidential data
therein. Zero trust.

It gets even more interesting when considering the spreadsheet being managed by our friends Alice and
Bob. Only one copy of the spreadsheet exists; both Bob and Alice can be looking at it and working on it,
but each see and have access to only the data appropriate to their credentials. Technically, Bob would
not even know he’s not seeing all the data. Again, zero trust.

The Implications of Zero Trust for Data
So, what would this mean for an organization and its data?

First, that data – all data, across mixed sensitivities – would be better protected. Because silos are
eliminated, all data can be co-located, improving efficiency, and making information immediately available
for use. Because we’ve now got fine-grained control, we can even apply this zero trust and ABAC to
search, so that all data, regardless of sensitivity, can be readily indexed and found; users only see the
results they’re authorized to see. And data scientists can focus on the objectives for their AI and analytics
work, instead of the infrastructure.

If this sounds like fantasy, it’s not. It’s actually the approach that prominent three-letter government
agencies use when they have to work with data of mixed sensitivities. That zero trust for data is now
making its way into commercial and government organizations of all types, and it promises to have a
major impact on how we work with – and protect – data going forward.

Cyber Defense eMagazine – September 2022 Edition 115
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