Published
November 19, 2025
About the Author
Justin Knash
Chief Technology Officer at X-Centric
As CTO at X-Centric IT Solutions, Justin leads cloud, security, and infrastructure practice with over 20 years of technology expertise.
TL; DR
Agentic AI has sprawled from pilots into core workflows. These agents assist, act, move data, trigger payments, change configs. Yet, most firms still treat them like features, not identities.
That gap creates an “identity blind spot” whereby non-human actors operate outside familiar Zero Trust and IAM guardrails, often invisible to monitoring. Therefore, the risk is two-sided.
Ignore it, and a silent misconfiguration or leaked token can cost millions and trust. Overcorrect, and you stifle innovation and stall support, billing, or release cycles.
So, what is the alternative?
We argue that IT teams should make their AI agents first-class citizens of governance. Keep a living register, assign owners, insist on auditability, and scale autonomy only where outcomes are reversible and observable.
Introduction
Agentic AI is here.
Most technology vendors are marketing these agents as teammates, not tools. Hence, their use will only grow. According to tech research firm Gartner, “by 2028, 33% of enterprise software applications will include agentic AI, up from less than 1% in 2024, enabling 15% of day-to-day work decisions to be made autonomously”.
However, as the use of AI agents increases, so will the cybersecurity risks associated with them, such as shadow agents, privilege escalation, and data exfiltration.
But what are the implications of this shift for your cybersecurity posture? The article is a brief for executives to consider the risks of using AI agents before they start using AI agents in their enterprise workflows.
The New Face of Autonomy — From Copilots to Colleagues
1.1 Autonomy without accountability
The last year has seen a surge of agentic AI, systems that don’t just assist but act. Unlike copilots that suggest and summarize, agentic AI systems make autonomous decisions, trigger workflows, and update records in real time.
Agentic AI is on the rise, and they have evolved from being assistants to actors. Whereas Copilots assist in tasks, agents trigger workflows and act. Gartner predicts that by 2028, enterprise AI agents will be making an increasing number of decisions without human intervention.
Here’s how AWS describes the progression of AI agents.
Level 1 – Chain: Rule-based robotic process automation (RPA)
Level 2 – Workflow: Actions are pre-defined, but the sequence can be dynamically determined
Level 3 – Partially autonomous: Given a goal, the agent can plan, execute, and adjust a sequence of actions
Level 4 – Fully autonomous: Operates with little to no oversight across domains, proactively sets goals, adapts to outcomes, and may even create or select its own tools.
The maturity of AI agents to level 4 and projection captures both the promise and the risk.
When machines act with delegated authority, they become part of your identity fabric, yet most organizations still treat them as “features,” not actors. The result is an emerging identity blind spot. A growing population of non-human identities (NHIs) that bypass traditional IAM, Zero Trust, and change management controls.
Before your team pushes forward with autonomous AI strategies, executives need to pause and ask: Are we operationally, legally, and culturally ready for this level of autonomy?
1.2 Assistive AI vs. autonomous agents
Until recently, AI existed inside the guardrails of human supervision. Generative copilots embedded in productivity tools operated under user credentials, acting only within visible interfaces.
Agentic AI changes that equation.
These agents can plan, reason, and act across systems. They integrate APIs, trigger automation scripts, and even initiate new data exchanges, all without waiting for human confirmation. Vendors market them as “digital teammates,” a phrase that feels friendly but obscures a harder truth: once an agent can act, it carries privileges. And privilege, whether human or not, is a security concept first.
This shift from assistive to autonomous blurs long-standing accountability lines.
When a developer deploys an agent that can create records or modify configurations, is that agent governed by the same lifecycle as a human user? Does it get offboarded, audited, and logged? In most environments, the honest answer is no. Hence, the enterprise is evolving faster than its control systems.
1.3 The invisible risk perimeter
Every new technology wave expands the attack surface. In the era of agentic AI, risk is concentrating at the edges of enterprise identity, the points where automation intersects with authority.
See also: Practical exercise to assess external attack surface exposure
Agents often live in cloud consoles, run as service accounts, or connect through over-permissive API keys. Many inherit administrator roles by convenience. Others are spawned by third-party platforms that no one in security has reviewed. Some use personal tokens tied to developers’ accounts.
Individually, these shortcuts look harmless. Collectively, they represent a shadow ecosystem of actors that no one monitors, revokes, or even counts. A rogue or compromised agent could exfiltrate sensitive data, escalate privileges, or make configuration changes that cascade across systems. And because their behavior often mimics normal automation traffic, traditional detection tools rarely flag them.
Security models built around human verification weren’t built to govern entities that learn, act, and adapt at machine speed.
Rethinking Governance and Trust in Agentic Systems
2.1 Governance, accountability and ownership of AI agents
When every business unit experiments with its own automation, the first casualty is ownership. In many organizations, it’s unclear whether IT, security, or business operations should define policies for agentic systems. It results in fragmented responsibility and delayed oversight.
True readiness starts with assigning stewardship. Agentic AI should fall under the same policy umbrella as other privileged automation, with one accountable executive maintaining a living register of all non-human identities. That register, no matter how imperfect, is the foundation for control.
See also: Connect governance and internal hygiene using Internal Vulnerability Assessment & Risk Prioritization.
But governance is also impacted by the organization’s cybersecurity culture. The organizations that handle autonomy well are those that treat AI as a colleague with duties, not a gadget with potential. They ask for proof of responsible deployment, regular attestation of access, and clear offboarding procedures when an agent is retired.
Ownership precedes oversight. Without it, even the most advanced controls are cosmetic.
2.2 Identity and access management for AI agents
Agentic AI complicates the logic of identity. Traditional models assume that a user’s access can be justified through human accountability, someone who logs in, authenticates, and accepts terms of use. Agents, however, authenticate through tokens or service principals and can act continuously.
Zero Trust models, built on user verification and device health, offer limited coverage. Conditional access policies rarely apply to a process running in the cloud. Secrets may never expire. A single misconfigured role can grant an agent more reach than any individual employee.
The challenge is conceptual.
You can’t extend human trust paradigms to entities that lack presence or intent. The only durable posture is purpose-bound access. Define what an agent is meant to do, grant it the minimum scope to do it, and ensure those privileges expire by design.
This doesn’t require a new IAM system overnight. It requires reframing the question from “can this agent work?” to “under what identity, with what scope, and who will be accountable if it misbehaves?”
2.3 Monitoring and response framework for AI agents
Most detection frameworks assume that anomalies originate from people. But agentic AI introduces behavior that’s synthetic by design: continuous, repetitive, and self-initiated.
Many SIEM and EDR platforms can’t easily distinguish a legitimate agent action from a malicious one if both use valid credentials. Logging gaps further complicate things. Cloud consoles may record API calls but not the reasoning chains behind them, leaving security teams blind to why an agent acted.
For now, the simplest mitigation is observability before automation. Every agent entering production should emit auditable logs, its inputs, actions, and outcomes, into the same telemetry fabric as other systems. Leaders should insist on one readiness drill: revoke an agent’s credentials mid-task and watch how the organization responds. This grants you visibility into the AI agent workflows.
In short, response readiness ensures the system tells you when a failure occurs.
See also: For a relevant team exercise that strengthens detection and response maturity, please refer to X-Centric's Incident Response Readiness Assessment.
Legal, Operational, and Business Implications
3.1 Compliance and third-party risk: Obligations you already have
Even if regulators haven’t yet named “agentic AI,” their principles already apply. Frameworks such as ISO 27001, SOC 2, and HIPAA all expect demonstrable control over who—or what—can access sensitive data. That includes agents operating on behalf of your company.
The same applies to vendor contracts. If a supplier deploys agents that touch your data, their terms should specify:
How are those AI identities logged?
How are breaches induced by AI agents reported?
Who bears responsibility for the unintended actions of AI agents?
Many organizations don’t realize that AI agents may qualify as sub processors under data protection laws. That means you’re responsible for ensuring transparency, auditability, and lawful processing—even if the agent belongs to a vendor.
Legal teams should begin by mapping where agentic systems intersect with regulated data.
What data do they process?
Where is it stored?
Who can access it, and for how long?
You may find that the quickest route to compliance is simply prohibiting autonomous writes to regulated systems until clearer guardrails exist.
Related reading: To understand how multi-cloud governance and compliance intersect, refer to the article “Enhancing Cloud Security Posture Management in a Multi-Cloud Environment”.
3.2 Business Impact and Prioritization: What’s at Stake
Risk, at its core, is a business conversation. Executives care less about technical vectors and more about outcomes: revenue loss, customer trust, and operational continuity.
The worst-plausible incident isn’t hypothetical. Imagine an autonomous agent misrouting invoice payment, exposing customer data through an integration misfire, or making unsanctioned changes to production systems.
The direct cost could be measured in millions; the reputational damage, far more.
Conversely, over-correcting can harm just as much. Quarantining or throttling agents may slow customer support or disrupt billing workflows (or anything you have assigned AI agents to do). The question becomes not “should we deploy agents?” but “which decisions are safe to automate, and which are too consequential to delegate?
Budget conversations should mirror that logic. The first dollars go toward visibility and inventory, knowing what exists and who owns it.
Governance automation, behavioral analytics, and advanced policy engines can come later. It’s cheaper to see clearly than to fix blindly.
3.3 Evidence that leadership can trust
When board members or CEOs ask for assurance, they don’t want dashboards; they want evidence that the basics are under control. A credible executive summary might show three things:
The ten most privileged agents and their accountable owners.
A simple comparison of intended versus actual permissions for those agents.
The backlog of stale or unused credentials and the plan to decommission them.
That one-page proof tells a story shows the board the three most important things leaders need to know:
The organization knows its attack surface
Manages its attack surface actively
Has a plan to monitor and mitigate emerging cybersecurity threats
Agentic AI demands believable evidence that oversight exists and can adapt.
Getting Ready for AI Agent Era
Every company faces a tension between innovation and risk. The pragmatic approach is to categorize use cases by reversibility.
Start with read-only or decision-support tasks—agents that summarize, enrich, or triage.
Move next to human-in-the-loop scenarios where the agent acts but requires confirmation. Only once those patterns prove safe and valuable should you consider autonomous ‘write privileges’ in core systems.
This staged adoption mirrors how organizations handled early automation and DevOps pipelines. Autonomy expands safely when it grows alongside trust, not ahead of it.
Executives should expect their teams to articulate these boundaries and revisit them quarterly. The goal is not to freeze progress but to scale governance as fast as innovation.
4.1 Preventing AI sprawl
Technology risk often stems from human incentives. When teams perceive governance as friction, they build around it. Agentic AI is no exception.
Preventing “Agentic AI sprawl” means designing an operating model where the safe path is also the easiest. That could mean a central approval workflow that feels lightweight, a shared registry that’s automated through APIs, or pre-approved templates that teams can use without delay.
Executives set the tone. A culture that rewards transparency—“show me your agent, even if it’s messy”—will surface risks early. One that prizes speed at all costs will only hear about problems after they’ve scaled.
The organizations that succeed will view this as an organizational readiness challenge, not a purely technical one. They’ll treat agent governance as an enabler of sustainable autonomy, not a constraint.
The Leadership Takeaway
Agentic AI isn’t waiting for policy to catch up. It’s already here, embedded in systems and processes that touch revenue and reputation.
Executives don’t need to master every technical nuance, but they do need to ask the right questions.
Who owns our agents?
How do they authenticate?
Can we observe the actions of AI agents?
What would happen if one went rogue tomorrow?
The answers will shape your risk posture and competitive agility. Companies that govern autonomy early will innovate confidently. Those that ignore it will find themselves reacting to invisible mistakes made by invisible actors.
Treat agentic AI as you would a new class of employee, one that never sleeps, never forgets, and never calls in sick. It deserves credentials, supervision, and consequences. The future belongs to the organizations that grant autonomy only to systems they can truly hold accountable.
What Next?
Book a 30-Min "AI Identity Risk" Briefing
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