#cybersecurity | hacker | Cloud Infrastructure IAM Lessons from the Capital One Breach

infrastructure is the foundation of more companies than ever. As with any
foundation, any crack can lead to significant damage to the infrastructure. One
potential crack is a trusted identity with unnecessary and excessive

A “trusted
identity” is invariably associated with people — employees, contractors or
other insiders. But identity in the cloud is no longer just about humans. The
proliferation of modern infrastructure driven by accelerated levels of
automation and innovation have led to an exponential rise in machine
identities, such as service accounts, bots, API keys, servers and applications,
and cloud resources. 

In the case
of Capital One, the identity that ultimately played a key role in the breach
was a machine identity in the form of an EC2 instance. Because the EC2 instance
had an over-provisioned identity and access management (IAM) role attached to
it (and many do), once the credentials were compromised, so were all the
privileges assigned to it.

That’s why
it’s so important to implement least privilege policies for all identities, but
especially for machine identities. Human identities can adapt their behaviors
to changing scenarios, but machine identities are not designed to do so and any
deviation in their behavior could indicate  
privileged credential misuse.

Hard Lessons from Capital One

As laid out
in an FBI indictment, the Capital One hacker accessed an EC2 instance (the
identity) via a misconfigured firewall and gained the ability to assume a role
on the machine. That role had the privileges to enumerate and download over 100
million customer records. Without those privileges, the damage would have had a
marginal impact.

How did the
EC2 instance become over-provisioned? Why did the assumed role have so many
high-risk privileges? It is pretty clear that Capital One’s authorization model
failed. The reason for this is twofold:

1.        The Fear of Under-Provisioning

Roles are
created with a broad set of assumed privileges based on a job description or
function within an organization. The problem is that it is almost impossible to
know or predict what privileges an identity actually needs, so most enterprises
err on the side of over-provisioning because they are afraid of negatively
impacting productivity. The problem is compounded as organizations change but
seldom update roles properly. The temptation is always to add a little more
into an existing role, rather than redesign the role completely. That is how we
end up with grossly over-provisioned machine and human identities.

2.     One to Many

When one
role is assigned to many identities (e.g. multiple EC2 instances), it almost
always implies over-provisioning. This happens because most identities will
only need a few privileges to perform their day-to-job. However, every time a
new identity is assigned that role, new privileges are added as well to
accommodate its function. The problem is that roles are seldom monitored and
reviewed and therefore privileges are rarely deleted. Without continuous
oversight, over time, a role will become massively over-provisioned.

The delta
between the privileges that identities need to successfully perform their day-to-day
jobs and the privileges they are granted is what we refer to as an avoidable

This is not
just a Capital One problem. Companies around the world are vulnerable to the
same threats, but we’ll use this example to illustrate how companies are
exposing their cloud infrastructure to excessive risk from insider threats.

So what can we learn from Capital One and similar incidents?

1.        Every machine identity in your
environment needs to be carefully examined to assess its potential risk to your
organization. How many high-risk privileges are assigned to the identity? What
privileges have they used over the last 90 days? What resources are they
accessing? Has the identity performed an unusual action on a new resource?

It’s also not
enough to assess once and move on. It must be continuous. If at any time there
is evidence that the machine identity is over-provisioned or is displaying
unusual behavior, take immediate action to prune or right size those
privileges. Continuously implementing and enforcing the principle of least
privilege across your cloud environment is the best way to keep it safe.

2.        When designing roles, don’t make
assumptions on what the identity might need to perform their day-to-day job.
Look for actual data to support your decisions such as identity activity
attributes. If an identity has not used a privilege for 90 days, there is a
good chance it doesn’t need it. In the Capital One case, two of the commands
that led to the exfiltration of data had never been used by the machine
identity (the EC2 instance). A data-driven approach gives security IT teams an
infinitely better picture of how to set up and maintain least privilege

3.        When creating a new role for an existing
identity, provision the set of privileges based on past usage. If it is a new
identity, start with just enough privileges of a similar identity or start with
a minimal set of privileges and gradually adjust. Review and right-size the
privileges continuously.

For example,
with this approach, the EC2 instance would not have had unfettered access to
the S3 buckets, preventing the hacker from downloading so many files at once.
Even if she had the ability to download a few files, the magnitude of the
incident would have been significantly smaller. To contain privilege creep with
roles, we need to either break down roles with a minimal common set of
privileges or create a unique role for each identity based on past usage.

these best practices will help improve your cloud security risk posture and
significantly limit the blast radius of an attempted breach such as what
Capital One experienced. Prevention is paramount, especially with cloud
infrastructure and it all starts with properly managing identity activity.

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