AI adoption is exposing cybersecurity response gaps in IT teams

At a glance

What this is: This survey report says AI is already embedded in most IT environments, but many teams still lack the response discipline needed when those tools are involved in security incidents.

Why it matters: For IAM, PAM, and NHI practitioners, the implication is that AI adoption changes the operating model for access, incident response, and recovery, not just the tooling mix.

By the numbers:

• 60% of organisations are already leveraging AI tools in their IT infrastructure.
• 51% of respondents confirmed experiencing a security incident in the past 12 months that demanded a dedicated response from security teams, not just an automated remediation.
• 75% of respondents reported financial damage due to attacks.

Context

AI adoption is now part of everyday IT operations, but that does not mean security teams have adapted their governance, response, or recovery models to match. When AI tools are folded into infrastructure and operational workflows, existing assumptions about control ownership and incident handling start to fray.

Netwrix's 2025 cybersecurity trends report is useful because it connects adoption to operational strain, not just enthusiasm. The broader question for identity teams is whether access governance, privileged control, and incident response can still work when AI use is already embedded in the environment.

The report's findings suggest the typical organisation is not dealing with a future-state AI programme. It is already operating in a mixed environment where AI use is present, security incidents still require human-led response, and financial impact is rising.

Key questions

Q: How should security teams govern AI in cybersecurity operations?

A: Security teams should govern AI in cybersecurity operations as a workflow control, not just a detection feature. Define where AI may summarise, prioritise, or route work, then keep approval authority, access changes, and exception handling under explicit human or policy control. This prevents convenience from quietly becoming delegated authority across the security programme.

Q: Why do incidents involving AI tools often need dedicated security response?

A: Because automated remediation rarely resolves the full scope of access, data, or configuration impact on its own. A dedicated response is needed when teams must inspect identity use, isolate affected accounts, and decide whether revocation, rotation, or business continuity measures are required. That is usually a sign the control plane did not fully contain the event.

Q: What breaks when AI adoption outpaces governance?

A: What breaks first is attribution. Teams lose visibility into which tools are in use, which data they can reach, and which actions were taken automatically versus manually. Once adoption is ahead of control design, security teams end up retrofitting policy around live workflows instead of governing them from the start.

Q: How can teams reduce financial damage from AI-enabled incidents?

A: They need to shorten the path from detection to containment by clarifying privilege scope, recovery ownership, and escalation authority before an incident occurs. Financial damage rises when teams cannot quickly isolate affected workflows or verify which AI-assisted actions were legitimate. The faster the organisation can prove control, the less likely the incident is to spread into business loss.

Technical breakdown

AI adoption in IT infrastructure and the governance gap

AI adoption in infrastructure is not the same as AI governance. Once AI tools are used in operational IT settings, they can influence configuration, data access, troubleshooting, and remediation paths, which widens the set of identities and workflows that matter to security. The governance gap appears when organisations assume the presence of AI tooling automatically improves control maturity. In practice, the control plane must account for who can invoke AI, what data it can reach, and which actions still require human approval.

Practical implication: inventory where AI tools sit in operational workflows before treating them as ordinary IT automation.

Why AI-related incidents still need dedicated security response

A dedicated response is required when automated remediation is not enough to contain the incident or establish root cause. That usually means the security team needs to investigate access paths, isolate affected systems, and determine whether AI-assisted actions changed the blast radius or obscured the chain of custody. In identity terms, the issue is not only what the system did, but who or what had the authority to do it. That makes response playbooks incomplete if they only cover technical failure and not delegated access or elevated control paths.

Practical implication: update incident runbooks so they separate automation failure from identity and privilege failure.

Financial damage as an identity and recovery signal

Financial damage is often the downstream signal that governance and containment failed together. Once an incident reaches business impact, the organisation has usually lost either speed, visibility, or authority over the affected environment. For identity teams, that is a recovery problem as much as a detection problem: access may remain too broad, approval paths too slow, or ownership unclear during containment. The report's trend line indicates that AI adoption is happening faster than the operational controls needed to limit loss.

Practical implication: tie recovery planning to privilege scope, system ownership, and approval speed, not just backup and restore.

Threat narrative

Attacker objective: The likely objective is to turn operational complexity and delegated access into broader disruption, faster response fatigue, and measurable business loss.

1. Entry occurs through AI-enabled IT workflows that are already embedded in operational environments, increasing the number of paths an attacker or failure can touch.
2. Escalation follows when the organisation relies on automated remediation but still needs a dedicated security response to investigate access, actions, and affected systems.
3. Impact is measured in financial damage, showing that the incident affected business outcomes beyond the original technical event.

Breaches seen in the wild

• Sisense breach 2024: A credential in Sisense's GitLab reportedly opened S3 buckets of customer tokens, passwords and certificates; CISA urged a full reset.
• CISA Private-CISA GitHub leak 2026: A CISA contractor's public GitHub repo exposed AWS GovCloud admin keys, Artifactory credentials and plaintext passwords for six months.

Read and download The State of NHI & AI Agent Breach Report 2026, covering 150+ breaches impacting Non-Human Identities including AI Agents.

NHI Mgmt Group analysis

AI adoption has outpaced the governance model that supports it: The report shows AI tools are already embedded in IT infrastructure, but the operating assumptions behind incident response and access control have not kept up. That is not a tooling problem alone. It is a sign that identity, approval, and recovery processes still treat AI-enabled operations as exceptional rather than normal, which is now a programme design flaw.

Dedicated response is the real test of operational maturity: If a security incident still needs human-led investigation after automated remediation runs, the control problem was never just technical automation. The response model must understand who had authority, what was changed, and whether delegated actions expanded the blast radius. Practitioners should treat this as a signal that incident handling is still disconnected from identity governance.

Financial damage is the downstream evidence of weak identity boundaries: The article's damage figures point to failures in containment, not just detection. When privilege scope, system ownership, and recovery authority are unclear, losses spread faster than teams can reverse them. That makes identity governance part of resilience, not a separate control family.

Operational AI creates an identity blast radius: Once AI tools are used inside IT workflows, the relevant security unit is no longer just the system or the user. It is the combination of access, approval, and automated action. Practitioners need to see this as an identity blast radius problem, where every added workflow dependency expands the area that must be governed before an incident occurs.

Existing cyber resilience programmes now need AI-aware control boundaries: The report signals that cyber insurance, response planning, and recovery procedures are all being stress-tested by AI adoption. Organisations that still separate AI operations from identity governance will keep discovering the gap only after an incident forces a dedicated response. The practical conclusion is that AI use now belongs inside the core identity and resilience programme, not beside it.

From our research library:

• Organisations that describe themselves as confident in their AI deployment actually experience a 72% security incident rate, compared to 33% for those who remain cautious, according to the 2026 Infrastructure Identity Survey.
• Only 13% of organisations feel extremely prepared for the reality of agentic AI despite the majority racing toward autonomous adoption, according to the 2026 Infrastructure Identity Survey.
• Read next: Agentic AI Security Guide

What this signals

AI adoption in infrastructure now carries an identity governance cost: once AI is embedded in operational IT workflows, the programme has to govern access, approval, and recovery together rather than as separate workstreams. Teams that keep AI under a tooling banner will miss the point at which automation starts changing who can act, not just how fast they act.

Response models built for conventional automation will keep failing at the edge of AI use: incidents involving AI-supported workflows can look routine until teams need a human decision to establish scope, isolate access, or verify action history. That is where incident handling, PAM, and identity governance converge, and where the current operating model often breaks.

For practitioners

• Map AI-enabled IT workflows Document where AI tools influence configuration, triage, remediation, or access decisions so the security team knows which workflows require governance and human oversight.
• Separate automation from authority Review which actions remain automated and which still depend on privileged human approval, especially where AI tooling can trigger changes in production systems.
• Update incident runbooks Add decision points for access review, escalation, and chain-of-custody checks when an incident involves AI-supported operational workflows.
• Tighten recovery ownership Assign clear system and privilege owners for AI-involved workflows so containment and restoration do not stall when teams must act quickly under pressure.

Key takeaways

• AI adoption in IT infrastructure is no longer speculative, but the governance model around it is still immature in many organisations.
• The report links AI use to a dedicated security response requirement and to financial damage, showing that operational maturity matters more than adoption speed.
• Identity governance, incident response, and recovery planning now need to account for AI-enabled workflows as part of the normal control surface.

Key terms

• AI-Connected Workflow: An AI-connected workflow is any process in which an AI system, model, or agent can read, transform, or route sensitive data as part of business operations. The governance challenge is that these workflows often inherit access from humans or services without a clear business justification for every permission.
• Dedicated Security Response: A human-led incident response effort that goes beyond automated remediation. It usually means the event requires investigation, containment, and recovery decisions that cannot be safely completed by scripts alone, especially when identities, credentials, or privileged actions are involved.
• Identity Blast Radius: The amount of damage a compromised identity can cause across systems, data, and infrastructure. In NHI environments, it is shaped by permissions, network reach, and administrative capability rather than by the credential alone. Reducing blast radius is a containment strategy that limits lateral movement and data exposure.

Deepen your knowledge

NHI governance, agentic AI identity, and machine identity lifecycle are core topics in our NHI Foundation Level course, the industry's only accredited NHI security programme. If you are building or maturing an IAM programme, it is worth exploring.