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Introduction: The Silent Expansion of Machine Identity
In modern enterprise environments, a new class of identity is quietly expanding faster than most security teams can track. At the center of this shift are AI agents and Non-Human Identities (NHIs), now operating with real privileges across cloud, SaaS, and on-premises systems. A recent survey conducted by Keeper Security at Keeper Security during Infosecurity Europe 2026 reveals a widening gap between adoption and governance, exposing a structural weakness in how organizations manage machine-driven access.
Survey Snapshot: What the Data Actually Reveals
The findings come from 86 cybersecurity professionals interviewed directly on the conference floor in London. While the sample is relatively small, it reflects a high-quality snapshot of real-world enterprise concerns. The results show a clear contradiction: AI-driven access is widespread, but visibility and governance remain fragmented, inconsistent, and in many cases, incomplete.
AI Agents Are Already Privileged Inside Enterprises
A striking 68% of respondents confirmed that AI agents or AI-powered tools already function as privileged identities within their environments. These are not experimental systems anymore; they are actively performing tasks with elevated permissions. However, only 15% of organizations claim full visibility across all environments, including cloud, on-premises, and SaaS platforms. This imbalance signals a dangerous blind spot where machine identities operate faster than oversight mechanisms can track.
The Visibility Gap: Security Teams Are Losing the Map
Despite rapid adoption, 65% of respondents identified limited visibility into AI and automation-driven access as a core security concern. This is not just a tooling issue; it reflects architectural fragmentation. Organizations are running multiple identity systems simultaneously, often without centralized control. As AI agents proliferate, each unmanaged endpoint becomes a potential entry point for attackers.
Fragmented Governance: No Single Source of Control
Only 14% of organizations manage NHIs through a centralized platform. The majority rely on scattered tools and inconsistent ownership models. In fact:
39% report unclear or shared ownership
33% operate with distributed but defined ownership
55% treat AI identities as privileged only in select cases
18% do not treat AI agents as privileged identities at all
This fragmentation creates an environment where accountability dissolves across systems, making incident response slower and less effective.
Security Incidents Are Already Happening
More than half of respondents reported experiencing a security incident involving NHIs or credentials in the past year. Even more concerning, 8% described those incidents as having significant business impact. Only 18% of organizations have continuous automated detection and response systems in place for NHI behavior. Meanwhile, 13% do not monitor NHI activity at all, leaving entire layers of machine access effectively invisible.
Standing Privileges: The Hidden Structural Weak Point
A major concern highlighted by 55% of respondents is the presence of excessive or standing privileges. These are permissions that remain active even when not needed, creating persistent exposure. In environments where AI agents operate at scale, standing privileges multiply risk exponentially, especially when paired with limited monitoring or fragmented governance structures.
Investment Is Increasing, But the Gap Remains
Despite current weaknesses, 64% of organizations plan to increase investment in securing NHIs and AI-driven access within the next 12–24 months. Another 22% anticipate significant strategic investment, while 41% expect incremental improvements. This indicates awareness is growing, but the speed of adoption may still be slower than the expansion of AI-driven systems themselves.
What Undercode Say:
AI agents are evolving into full enterprise identities faster than security frameworks can adapt
Visibility remains the single most critical failure point in modern identity security
Fragmentation across tools is weakening accountability chains
Machine identity is no longer experimental, it is operational
Most enterprises still treat NHIs as secondary security objects
Attack surfaces are expanding silently through automation pipelines
Centralized identity governance is still a minority practice
Cloud-first architectures are outpacing identity control systems
Security teams are reacting instead of predicting identity risks
Monitoring gaps create blind zones for automated systems
Privilege sprawl is becoming structural, not accidental
AI agents often inherit excessive permissions by default
Detection systems are not designed for machine-to-machine behavior
Incident response delays increase with identity fragmentation
Security ownership ambiguity slows remediation cycles
Many organizations lack unified identity inventories
SaaS environments introduce unmanaged identity duplication
On-prem systems remain disconnected from cloud identity policies
AI adoption is outpacing governance maturity by design
Most policies are human-centric, not machine-centric
Machine identity lifecycle management is still immature
Audit trails for AI actions remain inconsistent
Credential-based attacks are shifting toward non-human vectors
Security budgets are reactive rather than structural
Real-time identity analytics adoption is still low
Privileged access management is not fully adapted for AI agents
Cross-platform identity correlation is insufficient
Threat modeling rarely includes autonomous agents
Organizations underestimate automation-driven lateral movement
Detection latency is a key exploitable weakness
Identity sprawl increases with each new AI integration
Governance models lack standardization across industries
Security tooling ecosystems remain overly complex
Manual oversight is unsustainable at current AI scale
Automated remediation systems are underused
Policy enforcement inconsistency creates exploitable gaps
Machine identities are treated as extensions, not actors
Visibility tooling lacks real-time synchronization
Security culture has not fully adapted to AI identity risk
The identity perimeter has effectively dissolved
❌ The survey size (86 respondents) is small and not fully representative of all European enterprises
✅ AI agents are increasingly being integrated with privileged access in real enterprise environments
⚠️ Reported trends on visibility gaps and governance fragmentation align with broader cybersecurity industry concerns, but exact percentages may vary across studies
Prediction:
(+1) AI-driven identity governance platforms will become a standard enterprise security requirement within the next 2–3 years 🔐
(+1) Demand for unified NHI visibility tools will accelerate rapidly as incidents increase 📈
(-1) Organizations relying on fragmented identity tools will face rising breach frequency and longer detection delays ⚠️
Deep Anlysis (Commands & System Perspective):
To understand and manage Non-Human Identity exposure in enterprise environments, security teams increasingly rely on system-level inspection, logging, and identity correlation workflows.
List active machine identities and service accounts cat /etc/passwd | grep -i "service"
Check active privileged sessions (Linux)
who -a
Audit cloud IAM roles (AWS example)
aws iam list-roles
Detect unusual authentication patterns in logs
grep "Failed password" /var/log/auth.log
Inspect running AI or automation services
ps aux | grep -i ai
Monitor network connections from service accounts
netstat -tulnp
Review Kubernetes service account permissions
kubectl get serviceaccounts --all-namespaces
Check SaaS audit logs (generic API call example)
curl -X GET https://api.example.com/audit/logs
Identify long-lived tokens or standing privileges
find / -name "token" 2>/dev/null
Track identity usage over time
journalctl -u identity-service --since "24 hours ago"
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References:
Reported By: www.itsecurityguru.org
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