Healthcare security and IT teams are under constant pressure to protect patient data, maintain system uptime, and stay compliant with regulations like HIPAA and HITECH. At the same time, hospitals face growing cyber threats from ransomware, phishing, and insider misuse, while relying on manual processes like access reviews, log audits, and patch tracking.

In one industry survey, 93% of healthcare data breaches were linked to human error or internal misuse. Small teams, disconnected systems, and outdated workflows make it easy for gaps to go unnoticed.

Kindo helps close those gaps with intelligent automation. By connecting to your identity providers, EHRs, and IT tools, and applying large language models (LLMs) to the data, Kindo automates repetitive security tasks with speed and accuracy. 

Here are three high-impact workflows we’ll show you how to automate:

1. Automated Access Reviews. Identify excessive or outdated user permissions across EHRs and clinical systems using identity data and role-based policy checks.

2. Threat Detection Across Systems. Correlate logs from EHRs, identity providers, endpoints, and networks to find suspicious activity using LLM-powered analysis.

3. Patch Compliance Reporting. Monitor unpatched Windows devices by checking update status and check-in history to flag systems that pose operational risk.

These AI-driven playbooks help teams move faster, reduce risk, and stay compliant without relying on manual reviews or spreadsheets.

1. Automate Access Reviews for Clinical Systems

Hospitals and clinics rely on hundreds (often thousands) of user accounts spanning EHRs like Epic or Cerner, imaging and lab systems, and third-party applications. Keeping track of who has access to what is a monumental task – yet it’s important for patient privacy and regulatory compliance. HIPAA’s Security Rule requires that access to electronic PHI be granted based on a user’s role and reviewed regularly. In practice, however, many healthcare providers conduct access reviews infrequently and by hand (e.g. spreadsheets sent to managers), which often lag behind staff changes. The result is orphaned accounts and excessive privileges lingering in clinical systems – especially given the rotating workforce of clinicians and contractors in healthcare. Dormant accounts or a nurse with unnecessary prescribing rights are ticking time bombs from a security standpoint. Automating access reviews helps organizations uphold the principle of least privilege and prevent unauthorized access to patient data. It also prepares you for audits by demonstrating compliance with HIPAA, HITECH, and Joint Commission standards on access control.

Workflow Steps (Access Reviews)

1. Configure a Kindo workflow to pull user and account lists from your identity provider (e.g. Okta or Microsoft Entra ID) and from key clinical applications (Epic, Cerner, PACS imaging systems, etc.). This gives a consolidated mapping of who has access to which systems.

2. Use a Kindo LLM action to automatically cross-check each user’s access against their role or job title. The large language model can be prompted with your role-based access policies – for example, “Should a staff nurse have medication ordering privileges?” – and flag any deviations. This intelligent review goes beyond simple rules, catching subtle mismatches (e.g. a lab technician account with admin rights).

3. Have the workflow generate a clear report of out-of-scope or stale accounts. Kindo can format this as a CSV highlighting which users have permissions that don’t align with their role or department. This effectively means that each manager only sees the users under their purview, streamlining the certification process.

Value of Automation

Manually performing access reviews across dozens of systems might take weeks of chasing people and updating spreadsheets. By automating it with Kindo, reviews can be completed in hours with far greater accuracy. This reduces the window of risk caused by departing employees or role changes – inappropriate access is caught and revoked early, before it’s abused. It also provides an audit trail to prove due diligence. Every review cycle in Kindo is logged, with records of who approved which access. This makes it easy to demonstrate compliance with regulations like HIPAA (which mandates periodic review of access logs and user rights) and standards such as HIPAA 45 C.F.R. §164.308(a)(4)(i) on information access management and §164.312(a)(1) on access controls.

2. Detect and Investigate Threats Across Clinical Systems

Healthcare organizations are high-value targets for cyberattacks, yet their IT environments are notoriously siloed. A hospital might have an EHR system (Epic) generating its own logs, a separate identity system (AD/Okta) handling logins, network firewalls, medical device networks, and more – all producing disconnected streams of data. Traditional security operations centers struggle to get a unified view of suspicious behavior in this fragmented landscape. In fact, early warning signs often slip through the cracks: for example, a malicious insider or compromised account may start accessing patient records after-hours or in bulk, but if EHR access logs aren’t being actively correlated with other alerts, it could go unnoticed for months. Likewise, an outside attacker might use stolen credentials to log into a clinical portal from an odd location and then exfiltrate data, but the pattern may only become clear when authentication, EHR, and network logs are viewed together. The fallout from missed incidents is huge – healthcare breaches can expose tens of thousands of patient records and cost millions in fines and downtime. Security teams need a way to connect the dots faster across clinical and IT systems.

Workflow Steps (Threat Correlation & Response)

1. First, set up Kindo to aggregate event logs from all relevant sources. This can include EHR audit logs (e.g. patient record access events from Epic/Cerner), identity and access logs (Okta sign-ins, Active Directory events), endpoint security agents (CrowdStrike detections, antivirus alerts), and network devices (VPN, firewall logs). Kindo’s integrations or APIs will pull these into a common pipeline.

2. Use a Kindo LLM action to analyze the combined stream for suspicious patterns that span systems. The LLM can be given examples of concerning scenarios – “User logs into Epic from a new IP address and views 50 patient records in 10 minutes” or “Multiple failed VPN logins followed by an EHR access outside of shift hours” – and asked to find similar occurrences in the data. Unlike static SIEM correlation rules, the AI can reason across narrative patterns, linking what looks like isolated events into a potential incident.

3. When a potential threat is identified, the workflow enriches it with context. For example, if a doctor’s account accessed unusual records, Kindo can cross-check scheduling systems to see if that doctor was actually on shift or if an emergency access procedure (like break-glass access) was in effect. It can also pull the user’s role and typical access behavior to assess whether the activity is truly anomalous (if you defined the relevant API call in the workflow previously). This step reduces false positives by filtering out benign explanations.

4. Once the analysis is complete, Kindo generates a report that includes a severity rating for each incident (such as low, medium, or high). For incidents rated as high or showing clear signs of suspicious activity, the report includes a plain-language summary outlining what occurred, why it was flagged, and the relevant context behind the behavior. This summary is designed to be actionable for security analysts and can be delivered as part of a centralized incident report or exported into team tools like Slack, ServiceNow, or Jira.

Value of Automation

By correlating across traditionally siloed logs, this workflow identifies patient data abuse and lateral movement early – before a minor incident becomes a major breach. The use of an LLM for analysis means even nuanced attack patterns (that might evade simple correlation rules) can be caught, as the AI picks up on suspicious sequences of events in context. Automation also speeds up incident triage dramatically: instead of a security analyst manually stitching together logs from Epic, Active Directory, and a firewall (a process that could take hours), Kindo’s playbook does it in seconds and provides a natural-language summary. This reduces alert fatigue and ensures the team focuses on truly actionable threats. Finally, all incidents are documented in an easy-to-understand format for compliance. The workflow’s output can be archived to your Governance, Risk, and Compliance (GRC) system, creating ready evidence of security monitoring (addressing requirements like HIPAA’s mandate to review system activity logs).

3. Weekly Report of Unpatched Windows Devices

Ransomware actors famously prey on unpatched systems – the WannaCry outbreak that crippled many hospitals is a prime example, where supported but unpatched Windows machines were exploited en masse. Healthcare providers can’t afford to let known vulnerabilities linger. In the U.S., HIPAA’s Security Rule doesn’t mandate specific software or tools, but it does require organizations to identify and remediate security weaknesses as part of ongoing risk management. In fact, HIPAA’s administrative safeguards explicitly include procedures to “reduce risks and vulnerabilities” to e-PHI, which covers keeping systems up-to-date. Yet in practice, many IT teams lack a clear, timely view of which devices have fallen behind on patches. With dozens of clinics and thousands of Windows workstations, it’s easy for some to miss monthly updates or drop off the management radar. Without a proactive process, those gaps only surface during an incident or an audit – both of which you’d rather avoid. Automating a weekly patch compliance report ensures you continually find and fix missed updates before attackers do, and it provides assurance that you’re meeting the “reasonable and appropriate” security measures expected under HIPAA (§164.308(a)(5) technical safeguards on protection from malicious software).

Workflow Steps (Patch Compliance Report)

1. Set up a Kindo workflow (scheduled for, say, every Monday at 8 AM) that uses API actions to pull device and patch status data from your endpoint management system. For example, if you use Microsoft Intune or SCCM, the workflow can query for all Windows devices and retrieve fields like hostname, OS version/build, last check-in time, and the status of recent patches. This can typically be done via Intune Graph API or SCCM reports.

2. Have Kindo’s LLM analyze the data to spot devices that are not up-to-date. You can prompt the AI with the logic: “Find any Windows device that either (a) has not installed the latest monthly cumulative update, or (b) has not checked in to Intune in over 7 days.” The LLM will go through each device record and flag those that meet the criteria. It can also consider the severity of missing patches (e.g. if a critical OS security update is missing).

3. The workflow then formats the output into a simple, consumable report – for instance, a CSV file with columns for Device name, assigned user or location, days since last patch, and number of missing patches (or highest patch severity).

Value of Automation

This automated report gives IT a zero-effort weekly view of patch compliance across the organization. Instead of assuming things are getting patched, leaders have tangible data on a regular basis to drive remediation. Issues like a laptop that hasn’t checked in (perhaps due to being lost or offline) or a batch of PCs failing to install last week’s updates will immediately stand out. In our experience, making such information readily available greatly improves response times – desktop support can proactively reach out to get those devices fixed or isolated before an auditor or ransomware does. Moreover, it demonstrates proactive security. If regulators or internal auditors ask how you ensure prompt patching, you can show these automated reports and follow-up actions as evidence of your vulnerability management process. This helps fulfill HIPAA’s requirements around ongoing protection from threats (HIPAA Security Rule 164.308(a)(1)(B) and related safeguards) without specifying any single tool. Ultimately, automating patch compliance checks closes one of the most common doors attackers use, hardening your defenses with minimal extra effort. It’s an easy win that significantly lowers risk of preventable breaches.

Ready to Automate Your Healthcare Security Workflows?

Kindo gives security and IT teams the power to automate workflows across identity, clinical systems, and infrastructure, using LLMs that understand your environment and act intelligently. 

Whether you're hunting threats, tightening access, or proving compliance, Kindo helps you move faster and with more confidence.

See Kindo in action, book a demo or start building your first workflow today.

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