Report Shows Significance Of Machine Identity Security

Machine identity security is rapidly becoming one of the most critical aspects of modern cybersecurity strategies. While CISOs and IT leaders recognize its importance, many are still struggling to build a cohesive approach to protect these digital credentials.

Machine identities play a unique and foundational role in both modern and traditional systems, connecting devices, applications, APIs and cloud native technologies securely. However, as their use continues to expand, so does the complexity of managing them effectively. Machine identities now outnumber human identities by an overwhelming margin. This sheer scale is driven by several factors, including the rise of cloud native technologies, artificial intelligence (AI) and the shrinking lifespans of machine credentials in today’s fast-paced development cycles.

Each instance requires a unique identity to authenticate and communicate securely, adding to the already staggering growth in machine identities—particularly as organizations begin to embrace agentic AI. At the same time, malicious actors have taken notice, and cybercriminals are targeting machine identities as entry points for attacks. And machine identity attacks are growing.

By exploiting weaknesses in authentication systems or leveraging expired or mismanaged credentials, attackers can move laterally within networks, access sensitive data and disrupt critical operations. CyberArk recently unveiled findings from a survey report that examined the challenges around machine identities in greater detail. Their findings include:

When examining the business impact of a machine identify-related security incident:

The rise of AI has elevated the urgency of securing machine identities, with 81 percent of security leaders emphasizing its critical role in protecting AI’s future. Machine identities act as the gatekeepers, helping shield AI models and systems from threats like unauthorized access, model and data theft, and manipulation. A notable 79 percent of respondents agree that protecting AI models from compromise demands robust machine identity and stringent authentication and authorization protocols.

This focus reflects a growing awareness of how susceptible AI systems can be to exploitation. Manipulated models can generate harmful outputs, while stolen algorithms could endanger proprietary innovation and competitive advantage. Looking ahead, 72 percent of leaders predict a shift in priorities, moving from safely utilizing generative AI to directly safeguarding the models themselves. Additional findings included:

One of the reasons that machine identity vulnerabilities are becoming more commonplace is that there are simply more machine identities than ever—and that means more points of potential failure. It’s not surprising that 79 percent of organizations expect the number of machine identities to grow over the next year, with 63 percent projecting increases of up to 50 percent and 16 percent anticipating more aggressive growth between 50-150 percent per year.

Despite their overwhelming numbers and critical roles, only 23 percent of organizations prioritize securing machine identities exclusively—while 30 percent focus on human identities. Even though 47 percent treat machine and human identities as equal priorities, “equal” attention doesn’t necessarily reflect the growing scale or importance of machine identities.

Overall, machine identity security is widely recognized as critical, with 92 percent of security leaders reporting some form of a machine identity security program. Yet, this broad adoption doesn’t always equate to maturity. Many organizations face significant obstacles in delivering effective protection for their machine identities, including:

To complicate matters further, ownership of machine identity security remains fragmented. While 53 percent of security teams assume responsibility for preventing compromises, development (28 percent) and platform teams (14 percent) are still heavily involved. Similarly, other tasks such as managing certificates or creating policies are divided among teams, creating inefficiencies and gaps in management.

Aside from the emerging challenges of securing agentic AI, the future of machine identity security will be fraught with complex challenges, driven by major shifts such as certificate authority (CA) distrust events, shrinking certificate lifespans and the rise of quantum computing. Alarmingly, 71 percent of security leaders worry their CA could become untrusted at any time, and 46 percent fear the need for immediate reaction without adequate preparedness.

As we’ve highlighted before, attackers are zeroing in on machine identities, with 74 percent of security leaders worried that cloud native and development environments are becoming prime targets. The rapid adoption of both cloud native and AI technologies is further compounding the complexity of managing machine identities, amplifying the risks associated with protecting these critical assets.

The sheer scale and dynamic nature of cloud native environments present a unique challenge. With workloads spinning up and down in seconds, static and centralized management of machine identities falls short. Recognizing this, 73 percent of organizations are focusing on shifting towards a more distributed approach to managing and securing machine identities at the workload level. This shift ensures that even the most ephemeral environments have identities safeguarded in real-time.

Most organizations recognize the critical role of machine identities in securing systems and data, with 92 percent working under a machine identity security program and 44 percent planning to expand their use as a key component of their cybersecurity strategies.

However, the pace of progress may not be sufficient to counteract emerging threats. Automation is surfacing as a top priority, with 43 percent of respondents focusing on its deployment to handle the rising complexity introduced by shorter certificate lifespans. Managing certificates dynamically through automated processes will be essential for maintaining security without compromising business efficiency.

Visibility remains another critical challenge, with 39 percent reporting plans to improve oversight of all machine identities. Without clear insight into existing identities, gaps and blind spots can quickly turn into vulnerabilities. To underscore the growing emphasis on safeguarding AI assets from manipulation or theft, 39 percent plan to explore how machine identities can further protect Large Language Models (LLMs). Meanwhile, 35 percent of organizations are beginning to address the looming threat of quantum computing by preparing migration plans toward quantum-resistant encryption.