The rate at which the United States is aging is unprecedented. As aging demographics increase at high rates, senior citizens are requiring care and services faster than traditional models can keep up. To help meet this demand, healthcare organizations, assisted living providers, and home care services are turning to artificial intelligence solutions.

AI offers numerous opportunities to help extend care for seniors and aging adults. From socially assistive technologies to remote monitoring and predictive care insights. Companion robots, sensor suites, and clinical decision systems are actively expanding how older adults receive care.

The adoption of these technologies not only expands HIPAA risk but introduces new cybersecurity and privacy concerns under the CCPA. Organizations serving aging adults and the elder care markets need to understand the correlation between AI and CCPA risk.

 

Demand for Elder Care is Spurring AI Adoption

Demand is being driven by a combination of factors.

  • Aging baby boomers are living longer than previous generations
  • Chronic conditions are more prevalent than ever
  • Adults want to age in place instead of receiving institutional care
  • There is a severe shortage of caregivers at all levels

AI is quickly being adopted to meet this demand. From home care and assisted living to clinical adoption is expected to grow significantly over the next several years.

Within these settings, organizations are deploying AI-powered tools to help extend their reach and capacity while improving patient outcomes, satisfaction, and productivity.

 

 

How AI is Being Used to Deliver Elder Care

Technologies are being deployed to assist in a variety of ways.

Social Robots AI companions like ElliQ can reduce loneliness, provide reminders, stimulate activity, and connect with friends and family through everyday conversation.

Remote Monitoring Tools that track and analyze health information, catch early warning signs, and reduce the need for in-person checkups.

Clinical Decision Support AI is helping diagnose patients, triage cases, and, in certain instances, prescribe medications.

Smart Sensors Connectivity around the home and wearables can track movement, activity levels, and vitals to detect potential falls or health degradation.

Predictive Health Monitoring activity, motion, and physiology to provide predictive insights on health.

With these technologies come significant opportunities to transition care away from a reactive model towards one that is proactive and preventative.

 

 

AI is Expanding CCPA and Cybersecurity Risk

When we consider AI, it’s important to remember that these systems change the way information is generated, processed, and consumed. They introduce:

  • Constant monitoring of activity
  • Generation of biometric and physiological data
  • New inferences about health and wellness

Now consider how this might apply to elder care.

  • Behavior monitoring could provide information on daily activity patterns
  • Interaction data could be used to generate cognitive baselines
  • Sensor data could be used to infer health risks
  • AI may provide recommendations for care

Much of this information could be considered derived or inferred data under CCPA. This data may not traditionally be part of a medical record, but it is still regulated under both CCPA and HIPAA. Organizations need to be aware of how both areas of compliance apply to their AI usage.

 

Why Elder Care Organizations are More Vulnerable

From a cybersecurity and privacy standpoint, elder care faces unique challenges that put them at greater risk of a breach. Cybersecurity and privacy risk are increased by conditions such as:

  • Staff Shortages. There are not enough caregivers to keep up with demand, increasing dependency on technology.
  • Aging in Place. More care is delivered through connected homes and devices.
  • Fragmented TechStack. Devices, sensors, and applications silo information and create visibility gaps.
  • Accelerated Adoption. Technology is being purchased faster than internal policies and controls can be established.
  • Accessibility. Meeting specific patient requirements can require technology to be simplified, opening security gaps.

These conditions are expanding touchpoints for sensitive data. Making it more widely distributed across an organization makes it more vulnerable to attack.

 

 

Recent HIPAA breaches in Healthcare and Aesthetic Medicine Highlight Similar Weaknesses

While healthcare is no stranger to breaches, many continue to stem from third-party vendors and compromised cloud environments.  Large-scale patient data breaches are still occurring. Providers of senior care and elder technology solutions will find themselves in similar situations as they expand their technology footprint. The difference is that much of the data they collect is even more sensitive.

  • Monitoring technology can allow providers to watch patients 24/7. If this data is compromised, so is their privacy.
  • Behavior and health data can’t be reset like passwords or credentials. Once exposed, stolen information cannot be replaced.
  • Older adults may be more susceptible to fraud or other financial crimes.

Trust and privacy are paramount when serving this market. Cybersecurity exposures can put patient care at direct risk.

 

Examples of Key Cybersecurity Risks

Some of the key risks we’re seeing with the adoption of elder care and AI-powered tools include:

  • Third-party Vendors, AI Solution Providers, Cloud Platforms, Device Manufacturers
  • IoT Devices Connected sensors and wearable devices
  • Data Profiling & Inference AI-Generated Health and Behavioral Inferences
  • AI Explainability Systems lack transparency into how AI is making decisions
  • Shadow AI/Caregiver Adopted Tools, Devices, or applications deployed outside of a traditional IT environment

Again, this isn’t a list your team should simply check security controls against. Cybersecurity and technology risk in elder care has evolved. Focus should be placed on understanding how data is collected, processed, and used.

 

 

What’s at Stake if Organizations Fail to Manage Risk

While many of these risks ultimately lead to cybersecurity exposures, there can be serious implications for organizations that fail to implement strong protections.

Beyond traditional risk to data and finances, there are unique business impacts to consider.

Patient Safety. If monitoring systems are compromised, care could be delayed or endangered.

Loss of Independence. If technologies fail, seniors may need to rely on nursing homes or assisted living.

CCPA Enforcement. CCPA also includes a private right of action, which opens organizations to lawsuits if they fail to implement reasonable safeguards.

Reputation. As stated above, trust is everything. A breach could decimate your organization’s reputation.

Taken together, this further emphasizes the need to bridge the gap between IT and caregiving teams. Cybersecurity in elder care is no longer just an IT issue. It’s a business and care delivery issue.

 

 

Managing AI and CCPA Risk in Elder Care Technologies

Leading organizations are taking a structured approach to manage risks posed by their expanding technology usage.

  • Creating data flow maps to understand where data is stored across devices, systems, and vendors.
  • Identifying where AI is creating derived personal information.
  • Conducting vendor risk assessments to better understand their cybersecurity practices.
  • Implementing continuous monitoring to watch for anomalous behavior in connected environments.
  • Expanding security testing to cover data usage and storage, as well as AI decision-making.

 

 

HALOCK Can Help

At HALOCK, we have prebuilt frameworks and solutions that align with this approach. Our privacy risk assessment is designed to map how personal information is collected, used, and shared throughout your organization. Our AI Risk Assessment helps you understand how your AI systems could pose privacy, security, and operational risks.

Through these assessments, your organization can build a defensible, audit-ready approach to managing cybersecurity risk posed by your technology stack. Learn more about how AI is expanding the attack surface for elder care organizations. AI-powered technologies are quickly becoming foundational to how elder care is delivered. Not only does it provide seniors with opportunities for independence, but it also enables organizations to meet demand and improve outcomes.

As we’ve learned, though, these tools are expanding your cybersecurity and privacy risk. No longer can organizations take a predominantly reactive approach and wait for vulnerabilities to be discovered.

Understanding how your organization uses and shares data through these technologies is an important first step. Coupled with our Duty of Care Risk Analysis (DoCRA) framework, organizations can begin to build a justification and a defensible position for what reasonable security looks like in their unique environment.

Answering these questions and aligning your cybersecurity and AI risk management practices with DoCRA can help you better understand your current risk posture and build a reasonable security strategy.

 

Review Your CCPA Privacy Risk Posture

 

Elder Care Technologies & Trends With Artificial Intelligence (AI)

 

FAQ

How is AI currently being used in elder care?

AI is currently being used to assist with patient monitoring, companionship, diagnostics, and care coordination.

 

Is CCPA relevant to elder care organizations?

Yes, any solution that captures behavioral information or consumer data may fall under CCPA.

 

What are the biggest cybersecurity risks associated with AI in elder care?

The convergence of always-on devices, third-party platforms, and broad data collection creates huge risk.

 

Are derived insights created by AI considered personal information?

Yes, if they can be used to identify a patient, then it’s considered personal information under CCPA.

 

Where should organizations start with managing cybersecurity and AI risk?

Understanding where data touches your organization is a great place to start. From there, you can map AI-generated data and extend your cybersecurity program to include third-party vendors.

 

 

Key Terminology

CCPA. California legislation that expands on privacy rights and consumer data protection.

HIPAA. The law that regulates privacy and security around clinical data.

AI Risk. The risk that cybercriminals or vulnerabilities pose to your AI systems and collected data.

IoT. The Internet of Things refers to connected devices that capture and transmit data.

Data Inference. Information that is derived from collected data. Often considered personal information.

RPM (Remote Patient Monitoring). Platforms or technologies that track patient health remotely.

 

 

CCPA Privacy Risk Service