is an enterprise cybersecurity product expert specializing in risk detection and remediation.

In the evolving technology landscape, enterprises have invested more than ever in powerful automation, cloud computing, microservices and the IoT to serve their end users faster. Although these technologies propel the engine forward, if you’re an enterprise security leader in a regulated industry, you’re probably overwhelmed with the rapidly growing web of nonhuman actors. These actors, termed “machine identities,” are uniquely authenticated using certificates, keys, tokens and other cryptographic mechanisms and represent one of the most significant attack surfaces you may encounter. If you fail to manage them properly, they could quickly become an attacker’s cheat code to infiltrate enterprise assets. In this article, I’ll discuss machine identities as the next challenge for defenses and best practices for securing them.

The Surge In Machine Identities

According to researchers, the number of machine identities in enterprise environments is growing exponentially, outnumbering human identities by 45 to 1. Your application teams are using these programmatic services to not only automate mundane tasks like data synchronization but also drive complex intelligent workflows. For instance, bots can be used by your cloud operations team members to streamline DevOps processes, your support teams can automate their customer service requests and data engineers can conduct large-scale data mining, among many possibilities.

Your engineers also create APIs as the backbone to support required machine-to-machine communication capabilities, enabling streamlined interactions among applications, microservices and other devices. The proliferation is overwhelming in cloud-native environments with ephemeral containers and microservices constantly spinning up and down.

Why Machine Identity Management Is Critical

Traditionally, human identities have been simple enough to flag, as they follow predictable patterns. However, machine identities flip this paradigm upside down by operating at higher speeds and larger volumes and staying functional around the clock. Machine identities solely rely on keys, tokens and certificates that are often configured statically and act as an easy point of compromise. Hence, your security framework, including biometrics, passkeys and multifactor authentication (MFA), isn’t extensible for machine identities. In this context, let’s understand machine identities’ capabilities and access power.

First, APIs interface directly with applications and databases containing sensitive user data. When application teams mismanage API tokens, they can inadvertently lead to exposure of sensitive data and violation of privacy and industry regulations. Malicious bots with active service tokens can carry out attacks at a scale that no human can comprehend. A compromised bot’s identity can wreak havoc on systems that can go undetected for extended periods.

Second, attackers can use compromised machine identities to move from one system to another, exfiltering sensitive data. This movement can also bypass traditional security measures because they can blend in with regular traffic. Attackers also escalate privileges to more critical services.

Finally, attackers can inject malicious programs into a trusted supply chain using a compromised machine identity. In the 2020 breach involving SolarWinds, attackers injected malicious code into software updates distributed to Solarwinds’ enterprise customers, compromising vendor and customer security. Many enterprises also use third-party APIs that may not adhere to the best security standards.

Challenges In Securing Machine Identities

Your security team often needs help addressing the critical challenges of managing and protecting machine identities. To begin with, many enterprises lack the tools to detect, track and manage the overwhelming amount of bot and API activity. Without in-depth visibility, ensuring the security of those identities becomes extremely difficult.

Next, only authorized APIs and bots should have access to your organization’s sensitive data and critical assets but managing such exposure is complex. Many times, developers inadvertently leave static API keys and tokens hard-coded into application code, which makes them vulnerable to being stolen and misused. Machine identities create inherent risks when used widely without dynamic and scalable authentication methods.

Because bots and APIs are frequently created and then decommissioned, machine identities tend to have short life spans. Without life cycle management, they can quickly become the backdoors for attackers. Improper key management practices, like unencrypted private keys, shared or reused keys, or non-rotated keys, can also lead to security incidents.

Best Practices For Managing And Protecting Machine Identities

Your enterprise security team requires a comprehensive strategy to protect assets and sensitive data from incidents stemming from machine identities. Here’s how:

Adopt a zero-trust architecture (ZTA).

A ZTA framework emphasizes security as a “never trust, always verify” mechanism. Per this guideline, machine identities must be authenticated and authorized irrespective of their originating network. On top of authorization, such identities should only have access to the data necessary for their processes. This requires the security and application development teams to collaborate closely to generate tighter role-based access controls (RBACs), focusing on least privileged access to prevent overexposure.

Automate certificate and key management.

Managing machine identities via manual processes neither suffices nor scales. Your security team must invest in automated solutions to issue, track and renew certificates and keys at scale. Such practices are imperative for dynamic environments where machine identities need to be shortlived. Certificate-based authentication also ensures only trusted machines can communicate with each other.

Continuously monitor, audit and identify threats.

Monitoring machine identities involves detecting access anomalies, identifying both risky and compromised credentials and preventing malicious API usage. Security teams can also adopt advanced detection technologies, such as behavioral analytics and machine learning, to find these inconsistencies in real time. Additionally, auditing such identities is required for compliance purposes. Many regulatory frameworks, such as the Health Insurance Portability and Accountability Act (HIPAA), the Sarbanes-Oxley (SOX) Act and the Payment Card Industry (PCI) Data Security Standard, require organizations to maintain detailed records of how sensitive data is accessed and by whom.

The Future Of Machine Identities In Cybersecurity

The acceleration of automated services is inevitable for your enterprise to stay relevant in the market today. Hence, it would be best if you prioritized the security of machine identities alongside human identities. Adopting a proactive zero-trust approach and robust machine identity management will position you well to safeguard your digital ecosystems from emerging threats.

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