The world is experiencing a machine identity explosion. In an AI-first, hyper-automated enterprise, the majority of interactions no longer happen between humans and machines, but between machines themselves. From APIs to bots, service accounts, and serverless functions, machine identities have become the new front door to the digital enterprise. Yet most organisations are barely monitoring them.
According to SailPoint’s Machine Identity Research, 69% of companies now have more machine identities than human ones, with nearly half managing 10 times more machines than people. In a world driven by AI, edge computing, and real-time orchestration, machine identities are foundational, and they’re vulnerable.
The silent surge that is invisible, unchecked, and over-privileged
The proliferation of machine identities has been rapid and largely invisible. With every new cloud workload spun up or automation bot deployed, another machine identity is created. The issue confronting modern enterprises isn’t just the scale of these identities, it’s the unchecked sprawl and the velocity at which they multiply across systems.
Despite their growing numbers and critical roles, machine identities rarely receive the same governance rigour as human ones. While employees benefit from multi-factor authentication, role-based access controls, and routine access reviews, machines often operate in the shadows, lacking oversight, expiration protocols, or clear ownership. This governance gap has turned them into a goldmine for cybercriminals.
A key reason for the gap is the continued reliance on manual processes to manage machine identities. Without dedicated tools, security teams are left sifting through spreadsheets and piecing together data just to distinguish between human and non-human identities. Alarmingly, SailPoint research found that 66% of organisations say managing machine identities is more manual than managing human ones. As machine identities scale with cloud and AI adoption, the case for automation becomes not just compelling but critical.
Compounding the risk, machine identities frequently hold far more privilege than their human counterparts. They move data, execute workflows, access APIs, and in AI-driven environments, even make autonomous decisions. Left unmanaged through outdated credentials, hardcoded secrets, or orphaned service accounts, these identities become potent, persistent back doors into enterprise systems. Verizon’s 2025 Data Breach Investigations Report found that credential-based attacks remain a top initial access method, and attackers are increasingly targeting ungoverned machine accounts for entry.
Security blind spots are breeding breach and compliance failures
This growing exposure isn’t just a theoretical concern, it’s already leading to breaches and regulatory failures. With limited visibility into which machine identities exist, what access they have, and who owns them, organisations are often unable to detect misuse until it is too late. SailPoint’s findings show that 57% of organisations have inadvertently granted excessive access to machine identities, and 60% have faced compliance challenges as a direct result of poor governance.
Zombie accounts, where identities remain active long after their purpose has expired, are especially problematic. They often slip through audits and remain open to exploitation. These oversights are not just risky, they are costly. The average cost of a data breach in ASEAN reached an all-time high of SG$4.34 million (US$3.33 million) in 2024, representing a 7% increase from the previous year, according to an IBM study. The reputational damage and legal penalties tied to non-compliance can last far longer. As machine-to-machine communication becomes central to enterprise operations, securing machine identities must move from an afterthought to a cornerstone of cybersecurity and compliance strategy.
Automation in managing machine identities at scale
With machine identities now far outpacing human ones in most organisations, automation has become essential to managing them at scale. Modern approaches enable automation across the machine identity lifecycle, from initial discovery and classification to ownership assignment and access certification.
This means thousands of identities, ranging from service accounts to bots and RPAs, can be accurately grouped by application or function and assigned to accountable owners with minimal manual effort. Automating these processes not only saves hundreds of hours but also ensures that no identity is left unmonitored or over-privileged. While some interventions may still be required when source data is incomplete, the majority of workflows can and should be automated. In today’s high-stakes security landscape, automation isn’t just about efficiency, it’s fundamental to reducing risk, enforcing policy consistently, and maintaining compliance across dynamic digital ecosystems.
Machine identity security evolution and way forward
The identity landscape is evolving fast. To stay ahead, organisations must adopt approaches that provide comprehensive discovery, ownership assignment, unified governance, and automated certification — capabilities essential for managing both human and non-human identities at scale. Solutions with built-in intelligence and broad integrations across legacy and modern platforms are best placed to secure an expanding digital environment.
A strong identity security framework ensures all identities, whether human or machine, follow the same rigorous access controls and governance policies. As machine identities continue to grow in volume and complexity, identity security must evolve from an operational task to a strategic imperative; one that safeguards security, compliance, and digital autonomy.
