What is the Machine-First identity security approach

Introduction

Remember the good old days when keeping track of your office's identities meant just remembering the names of your colleagues and their extension numbers? Well, those times have changed and so has the world of identities.

Traditionally, identity security was human-centric, managed mainly through centralized solutions like Active Directory, focusing predominantly on workforce identities, such as employees and contractors. As organizations have migrated to the cloud, the identity spectrum has expanded dramatically. We now face many segmented IAM technologies, with a significant surge in machine identities like service accounts, API keys, secrets, third-party integrations, and AI Agents. This explosion is not just about numbers. It introduces a lot of unique challenges that traditional methods aren’t equipped to handle, such as shared accounts we don’t have control over or countless credentials lying around at risk of being stolen. In this blog, we will explore the current challenges in the identity space, introduce a new identity security approach, machine-first, and learn why adopting it will help organizations face the challenges of this identity crisis.

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The Evolution of Identities

In the past, identity security was like guarding a fortress. Organizations had strong network perimeters, and the primary focus was on employee identities. Identities were mainly managed using Active Directory, and programmatic interactions were managed through service accounts. The majority of machine identities that IT teams cared about were privileged users, using PAM solutions to monitor and control activities like employees accessing internal systems using SSH (Secure Shell) and RDP (Remote Desktop Protocol).

As organizations began migrating to the cloud, the identity management landscape expanded and diversified dramatically. The advent of cloud computing introduced a new era of identity challenges, characterized by the decentralization of IT resources and the fragmentation of what were once unified systems. Microservices architecture further compounded these challenges, breaking down traditional monolithic applications into smaller, independently deployable services. Each of these services potentially requires its own set of identities for access control, leading to an explosion in the number of machine identities like service accounts and API keys.

Simultaneously, the adoption of SaaS began reshaping enterprise application use, prioritizing ease and speed over direct IT control. Crucial SaaS applications such as CI/CD solutions and data warehouses created extensive use of API keys and OAuth tokens. These components became focal points for security concerns due to their ephemeral nature and the challenge of monitoring their widespread use across platforms.

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In this new era, the concept of identity extends beyond human users to encompass a multitude of non-human actors: automated processes, service accounts, SaaS applications, AI agents, you name it. This explosion of identities across fragmented environments sharply differs from the centralized, human-focused approaches of the past.

It’s not surprising that the most common method to attack organizations today is through identity-based attacks. In an era with numerous identities managed in disparate places, the attack surface has expanded, making it easier for adversaries to find a vulnerable entry point.

The Challenges of Machine Identities

The shift to cloud computing introduces numerous challenges in managing machine identities. These challenges include, but are not limited to:

• Dynamic cloud environments: The fluid and dynamic nature of cloud environments means that identities are constantly being created, modified, and retired, which can lead to issues such as stale identities or improper access controls.
• Complex Interactions: The interactions between various identities, both human and non-human, have become increasingly complex in cloud environments due to inner connectivity, different protocols, and their scale.
• Diverse Identity Management: With the adoption of IaaS and SaaS models, identity management has become fragmented across different platforms and services, leading to potential security gaps and inconsistencies.

These challenges manifest in several specific issues.

• ‍Shared accounts - Accounts, identities, and roles used by multiple workloads and people make it tough to keep track of who's doing what and who's responsible for it.‍
• Key rotation - It is vital for security and compliance but complicated since it’s hard to predict which systems might be affected or determine who really 'owns' an identity. Then there’s the issue of accidental access between different operational environments, like someone in a sandbox environment getting into the production space without intending to.
• Cross account access - Then there’s the issue of accidental access between different operational environments, like someone in a sandbox environment getting into the production space without intending to.‍
• Over-privileged identities are another common problem. Often, individuals who set up these identities and their permissions don’t specialize in security, leading to granting excessive access to what's actually needed, which can pose significant security risks.‍
• Stale Identities: In many organizations, numerous dormant identities exist. These include outdated or neglected credentials like access keys and secrets.  It’s like leaving spare keys to your house out in the open, which hackers can find and use to break in and steal your personal information.‍
• Partially off-boarded employees - the risk of former employees retaining access poses a persistent threat. This problem is compounded by employees who might use service accounts for activities outside their designated scope, amplifying security vulnerabilities. These examples highlight the multifaceted and intricate nature of identity management challenges in cloud computing.

The Machine-First Approach

It’s become clear that the traditional, human-centric approach to identity security isn’t quite up to par in the cloud era. We've seen big players in the industry, and even vendors specializing in these solutions, get hit by identity-based attacks, signaling a need for change - for example, Okta, CircleCI, Microsoft, LastPass, and the list goes on.

Why Machine-Centric is the Way Forward

The machine-first approach is about flipping the script. It starts with understanding the machines in your cloud setup, pinpointing who has access to what, and where your security might be lacking.

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Key Aspects of the Machine-First Approach

1. Discovery: The first step is comprehensive mapping, because you can’t protect what you don’t know. You need to identify every identity in your cloud space, both human and non-human. The cloud's fast-paced and diverse technologies demand a solution specifically designed to keep up with these changes. It's like having a dynamic blueprint of your cloud's identity landscape.
2. Attribution: Knowing the identities is just part of the puzzle. You need to understand who uses each identity - which workloads and which humans - and, importantly, who owns them. This insight is crucial before securing these identities or managing their life cycles.
3. No Interference: A pivotal requirement is that this solution must operate seamlessly within your production environment. It shouldn't disrupt how your workloads and cloud identities interact or slow down your development processes in the cloud.

By focusing on these key aspects, a machine-first approach equips you to better understand, manage, and secure your cloud environment. It's a strategy designed to navigate the complexities of the cloud without hurting the agility and innovation that cloud technologies enable.

In short, adopting a machine-first approach can give you a clearer picture and better control of your cloud security landscape. It’s a smart move in a world where cloud complexity only grows.

Embracing Machine-First Identity Security for Cloud Resilience

The shift to a machine-first identity security strategy is not just a trend but a necessary evolution in the cloud era. As cloud technologies continue to advance and the number and type of cloud identities increase, organizations must adapt their security strategies accordingly.

The future of identity security in the cloud is complex, demanding a nuanced understanding of a wide range of identities and the risks they pose.

By adopting a machine-first perspective, organizations not only safeguard their digital assets more effectively but also lay a foundation for adapting to future technological advancements and security challenges. In the end, embracing this approach isn't just about enhancing security; it's about ensuring that an organization remains agile, resilient, and prepared for the evolving landscape of cloud identity.