As artificial intelligence agents become increasingly embedded in clinical workflows – making decisions, accessing records, and interacting with patients – the traditional boundaries of identity and access management are blurring. In healthcare environments where clinical staff already represent a significant security risk due to complex workflows and high-pressure conditions, the introduction of AI agents acting on behalf of humans adds a new and underregulated attack surface.
These agents must be held to the same standards of accountability and oversight as their human counterparts. Human Risk Management (HRM) principles offer a path forward, providing a unified framework to govern behavior regardless of whether it’s driven by a clinician or an algorithm.
By focusing on behavior as the shared denominator, healthcare IT leaders can proactively address threats from both human and machine actors, closing a critical gap in clinical security strategy before it widens.
The Expanding Role of AI Agents in Healthcare
Artificial intelligence is no longer on the periphery of healthcare: it’s becoming an integral part of daily operations. From assisting in diagnostics and triaging patient symptoms to automating documentation and engaging in preliminary patient interactions, AI agents are helping streamline workloads in overburdened health systems. These tools promise faster decision-making, improved operational efficiency, and reduced clinician burnout by handling repetitive or data-intensive tasks.
But with this growing integration comes a new layer of risk. AI agents are increasingly operating with real authority, accessing sensitive patient records, generating treatment recommendations, and in some cases, initiating actions with minimal human oversight. Their presence in healthcare workflows introduces vulnerabilities that are both technical and behavioral. Overreliance on agents may lead to uncritical acceptance of their outputs. Worse, if an AI agent is compromised, it could expose vast amounts of sensitive data or act inappropriately in high-stakes scenarios.
Traditional identity and access management (IAM) systems are ill-equipped to handle these challenges. IAM frameworks were designed for human users, such as individuals with defined roles, credentials, and accountability structures. In contrast, AI agents often operate persistently, adaptively, and in complex environments where their “identity” is abstract and their “behavior” is governed by algorithms that evolve over time. This creates a gray area in access governance and security auditing, especially when it comes to determining responsibility in the event of an error or breach.
Compounding the issue is a lack of consistent regulatory oversight. There are currently no widely accepted standards for how AI agents in healthcare should be credentialed, monitored, or held accountable. As these agents become embedded in clinical care, the absence of clear guidelines leaves organizations exposed, relying on legacy systems to manage a fundamentally new class of risk.
HRM as a Unifying Framework
HRM is an approach designed to address the unpredictable, behavior-driven risks posed by people inside an organization. Rather than focusing solely on roles and access permissions, HRM takes a dynamic, behavioral view – identifying, prioritizing, and mitigating risky actions before they escalate into security incidents.
An HRM platform helps security teams detect patterns such as password reuse, susceptibility to deepfake audio, or repeated disregard for phishing warnings. Crucially, it enables targeted interventions like training or access adjustments before these behaviors result in a breach. As AI agents become embedded in healthcare environments, the same behavioral lens must be applied to them.
While HIPAA has long governed the actions of human users through mandates like workforce training and role-based access control, it does not yet address the behavioral risks introduced by increasingly autonomous AI agents operating in clinical environments. These agents can access PHI, make decisions, and initiate actions with limited human oversight—capabilities that fall outside traditional compliance auditing. HRM fills this gap by bringing a behavioral lens to both humans and machines, enabling organizations to detect noncompliant or anomalous behavior before it results in a privacy violation. In essence, HRM extends the intent of HIPAA by protecting patient data and ensuring accountability into the era of agentic AI.
Although these agents are not human, their actions mirror human decision-making in many respects like querying data, initiating processes, and sometimes even escalating privileges. Treating them as static entities with fixed permissions fails to account for the reality of how they operate in dynamic, real-world workflows. Just as clinicians might bypass security protocols under pressure or ignore alerts in a noisy environment, AI agents can execute unsupervised queries, retain access longer than intended, or trigger unintended consequences through automation. These behaviors, whether from a person or a machine, represent active risk.
By focusing on behavior, healthcare organizations can deploy monitoring systems that detect anomalies and flag risky patterns in real time, regardless of whether the source is human or artificial. Extending HRM principles to AI allows security teams to unify oversight under a single framework, closing critical gaps and ensuring that all actors in the clinical environment are held to the same standards of vigilance and accountability.
Operationalizing Behavioral Security
To close the widening gap, healthcare leaders should extend the same HRM guardrails that protect against clinician error to the machine actors now working beside them. A few practical moves that healthcare IT teams can take include:
- Audit the agents. Treat every AI decision, query, and data pull as log‑worthy. Feed these events into the same dashboards and alerting rules that monitor human activity.
- Blend the scores. Fold AI behaviors—frequency of access escalations, off‑hours queries, deviation from expected workflows—into your existing workforce risk‑scoring model so a single map reflects all actors.
- Codify accountability. Publish policies that assign ownership for AI outputs, require a clinical “sponsor” for each agent, and define escalation paths when an algorithm misbehaves.
- Nudge in real time. Deploy lightweight, context-aware prompts to steer humans and machines back to safe practice before risks metastasize.
Governance for a Hybrid Workforce
Because AI touches clinical efficacy, data privacy, and regulatory compliance, security cannot operate in a silo. IT, agents, compliance, and frontline clinicians must share a continuous feedback loop that refines risks and responses. The hybrid workforce is not a future scenario; it’s already happening. By adopting HRM as a scalable, behavior‑centric framework today, healthcare organizations can safeguard innovation without throttling it.
About Ashley Rose
As the CEO of Living Security, Ashley is passionate about helping companies build a positive security culture within their organizations. Living Security is the global leader in Human Risk Management (HRM), providing a risk-informed approach that meets organizations where they are—whether that’s starting with AI-based phishing simulations, intelligent behavior-based training, or implementing a full HRM strategy that correlates behavior, identity, and threat data streams.
