AI in Protecting Healthcare Data: A Threat or an Advanced Shield?

We are talking about this dual role of AI in healthcare data in our blog, discovering its long-term benefits and a few ways it can safeguard data. 

An Alarming State of Healthcare Landscape

Several major data breaches occurred in the first half of this year, impacting the lives of more than 100 million patients. The HIPAA Journal, in its report "H1, 2024 Healthcare Data Breach," said 387 breaches happened until June. This is 8.3% more incidents than the previous year. 

Most of these breaches were hacking/IT incidents that affected the network servers of large healthcare organizations. Data loss/theft and unauthorized access were also reasons. Amidst this, will using AIaaS solutions in the healthcare industry increase the breaches?

Is AI a Potential Threat to Healthcare Data?

AI systems are data-dependent, and in healthcare, data is very confidential. Patient information, such as medical history, diagnoses, and personal identifiers, requires strict security. AI systems may pose threats to this data, allowing malicious actors to exploit it. Here is why.

1. Interconnection to Other Systems 

AI systems are designed to extract and analyze data from other systems within healthcare networks. The more interconnected these systems are, the greater the risk of a cyberattack. 

Hackers may gain unauthorized access to patient data by targeting weak AI algorithms and machine learning (ML) models. 

2. Lack of Transparency in AI Models

It is hard to understand how AI algorithms make decisions. Therefore, they are called black boxes. Knowing the backend process will make compliance with HIPAA easier, and sensitive data may be exposed unknowingly. 

3. Data Ownership and Consent Issues

Massive datasets, sourced from multiple institutions or even across borders, raise serious questions about ownership. Knowing who owns the data helps manage healthcare data and implement proper protocols to manage user consent. Without this information, AI healthcare solutions may process data that patients did not consent to. 

AI is risky only if implemented poorly or uses solutions built by inexperienced engineers. However, if used responsibly, artificial intelligence can benefit in numerous ways. It can protect patient data beyond standard cybersecurity measures.

The Bright Side of AI in Healthcare Data Protection 

1. Improved Threat Detection and Response

Imagine noticing any data breach in real-time and stopping it immediately. Proactiveness requires constant data monitoring and skills to detect any malicious act. It is impossible for a human to constantly monitor vast and complex patient data. 

For AI, it is just a matter of minutes. It can scan huge amounts of data, no matter its complexity, and find anomalies or potential cyber threats in real-time. By doing so, AI can give early warnings and help you take necessary actions. 

Besides real-time monitoring, AI systems can identify the possibility of potential threats that may happen within days, weeks, or even years. So, you can discard those risks before they escalate into full-blown threats. 

2. Scalability

Your healthcare systems will deal with constantly new patient data and information. This growing database will increase the risk of cyber threats, too. You can monitor all data access points and networks by using AI in healthcare cybersecurity. 

3. 24/7 Monitoring and Automation

Cyber threats can happen anytime, any season. Your IT team must be attentive 24x7 to detect and respond to the threats. 

However, continuous monitoring is difficult and causes fatigue and burnout. AI-powered systems can work around the clock and reduce the likelihood of breaches. 

4. Efficiency in Data Management and Encryption

Storing and encrypting patients' sensitive information is essential for data safety, and using AI for healthcare data management and encryption can reduce human errors. 

AI algorithms automate encryption and decryption, making the process faster and preventing a single chance of data theft or loss. 

5. Faster Incident Response

The time it takes to neutralize a cyber attack also decides how much you will lose. More time means more loss to you and your patients. 

AI in healthcare cybersecurity can speed up the incident response plan to neutralize any attack faster than its impact. This quick reaction can reduce the amount of potential damage and prevent further breaches.

6. Reduced Human Error

Most of the data breaches happen due to human error or negligence. Misconfigurations, weak passwords, or password sharing are all the reasons behind common threats. AI can minimize errors by automating and authenticating processes that require manual intervention. 

7. Cost-Effective Security Solutions

One of the benefits of using AI in securing healthcare data is the lower cost. It would help if you had a large security team to perform security tasks such as incident responses, risk assessments, etc. 

Furthermore, there are penalties and legal liability that you may have to bear in case of any data breach. AI automates most of the security tasks, minimizing any chances of threats and reducing overall risk and costs. 

6 Ways to Use AI in Your Healthcare Data Protection Plan 

1. Anomaly Detection and Threat Detection

Electronic health records suffer more data breaches than any other healthcare system. They contain sensitive patient information, medical records, and more. Unauthorized access to EHRs can breach the confidentiality of data. 

Artificial intelligence in electronic health records can secure access and flag suspicious behavior. It can also detect potential threats outside the scope of traditional methods, giving EHRs robust security. 

2. Preserve Evidence

How you handle the breach is one part, but what you do afterward decides the future of the incident. AI helps you analyze and preserve evidence post-breach. It can identify the patterns leading to security breaches and recommend measures to strengthen security. 

3. Encryption and Secure Data Sharing

Different departments in your healthcare organization have access to the same data. For example, the medical records department and finance department need data from EHRs to process bills and claims. Any leakage of data while sharing between these departments can bring penalties to you. 

AI-driven systems implement robust encryption techniques to ensure the safe sharing of healthcare data, whether shared on-premises or in the cloud. This ensures that no unknown or unauthorized person can accidentally or intentionally access that data. 

4. Identity and Access Management (IAM)

Multi-factor authentication (MFA) is one of the most robust ways to secure healthcare data. AI strengthens MFA through voice and speech recognition and facial and fingerprint scanning. 

It can only allow authorized persons to log in to critical systems by recognizing their voice. This type of authentication ensures your hospital is free from insider threats. 

5. Fraud Detection and Prevention

Billing frauds are common in the healthcare industry. Processing and cross-verifying every bill is a tedious manual job. AI can identify fraud, such as upcoding and phantom billing (charging for unused services). It can also alert about fraud insurance claims and non-adherence to billing practices set by Medicaid, Medicare, or private insurers. 

6. Data Loss Prevention (DLP)

What if you know the source of data leakage or whenever data is copied without permission? You can act before it brings you a huge loss. AI solutions can give you that power. It detects and alerts whenever sensitive information is at risk. 

AI in Securing Healthcare Data is All About Finding the Right Balance 

How you see and use AI decides what role it will play for you. It can be a threat or a way to safeguard you from cyber attacks. Finding the right balance will help you use AI in healthcare data protection. 

It helps you manage the risks that are quite impossible, challenging, and even costly with traditional cybersecurity measures. 

To find this balance, you should do a few things:

• Define who owns the data and how it will be used.
• Regularly fine-tune your AI models and systems to strengthen them against advanced vulnerabilities and threats. 
• Keep your AI systems transparent in their working, from data collection to decision-making. 
• Partner with experts in AI and healthcare data security 

Conclusion

The use of AI in healthcare data protection is a must, considering the nature of data and the industry's rapid digitalization. Undoubtedly, some risk exists, but healthcare organizations should consider the positive side of AI. 

AI can predict the possibility of threats and find abnormalities that are difficult to notice. Traditional cybersecurity measures lack these capabilities, making using AI in securing healthcare data even more important.

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