Shadow AI and Secret Cyborgs: Unveiling the Challenges in Pharma, Healthcare, and Discovery
Gen AI can contribute $110 billion to the pharmaceutical and healthcare industry.
Growth in AI Adoption in Pharma and Healthcare
AI adoption in the pharmaceutical and healthcare sectors is rapidly increasing. According to a report by Grand View Research, the global AI in healthcare market size was valued at $15.4 billion in 2022 and is expected to expand at a compound annual growth rate (CAGR) of 37.5% from 2023 to 2030.
In the healthcare and pharmaceutical industry, technology has played a significant role in changing the way professionals approach patient care, drug discovery, and operational efficiency. Generative Artificial Intelligence (Gen AI) has emerged as a game changer, offering solutions that range from virtual health assistants to sophisticated drug discovery platforms. However, with great power comes great responsibility—and new challenges. One such challenge is the rise of “Shadow AI” and “secret cyborgs” within these sectors. This article delves into the complexities and potential risks posed by these phenomena, providing case studies and a thorough analysis to illustrate their impact on the industry.
Understanding Shadow AI and Secret Cyborgs
Shadow AI refers to AI systems and processes that operate without the oversight or knowledge of security teams, IT departments, governance bodies, or even the organizations that benefit from them. These systems may be used with well intentioned efforts by departments or individuals looking to streamline workflows or solve specific problems without the formal backing of the organization’s infrastructure.
On the other hand, secret cyborgs refer to a blend of human and machine where employees rely heavily on AI tools to perform their jobs, often without disclosing the extent of this reliance. This covert integration can lead to issues in accountability, transparency, and data integrity.
Both Shadow AI and secret cyborgs present unique challenges in the pharmaceutical and healthcare sectors, where the stakes are incredibly high. The following sections will explore these challenges in detail.
Cost of Data Breaches in Healthcare
According to the 2023 IBM Cost of a Data Breach Report, the average cost of a data breach in the healthcare sector reached a record high of $10.93 million per incident. This represents a 53% increase over the past three years. Healthcare continues to be the most expensive industry for data breaches for the 13th year in a row.
Volume of Data at Risk
The healthcare industry generates massive amounts of data, with estimates suggesting that by 2025, the global healthcare data volume will reach 2,314 exabytes. With such vast amounts of sensitive data, the potential impact of unauthorized AI tools processing this data without proper security measures is enormous.
Shadow AI – Navigating the Unseen Risks in Cybersecurity
The Rise of Shadow AI in Pharma and Healthcare
Shadow AI has increasingly found its way into the pharmaceutical and healthcare sectors, driven by the need for rapid innovation and efficiency. However, its presence brings significant risks and challenges.
- Data Breach
A data security incident could occur if a research organization experiences unauthorized access to sensitive health data due to the unapproved use of an AI tool. For instance, if a researcher deploys an AI tool that hasn’t been vetted by the IT department, it could lead to a significant data breach. In the pharmaceutical industry, an employee in the R&D department might use public generative AI tools to accelerate the drug discovery process. For instance, they might leverage AI to generate potential molecular structures or predict drug interactions, aiming to streamline early stage research. It can reduce several months of effort to a few days.
Benefits and Data Security Issues
While these AI tools offer considerable benefits, including enhanced productivity and the ability to rapidly process complex data, they also present significant data security challenges. Public generative AI tools, which are often hosted on third party platforms, may expose sensitive research data, including proprietary formulas or confidential patient information, to unauthorized access. The use of such tools without proper oversight can lead to data breaches, intellectual property theft, and compliance violations, especially if the data is not adequately encrypted or if the AI platform has insufficient security measures in place.
Volume of Data Impacted: Such a breach might expose several health records, including sensitive information like patient names, diagnoses, and treatment plans.
Estimated Cost: The financial impact could be substantial. Based on average costs of data breaches in healthcare, including regulatory fines, legal fees, and remediation efforts, the total cost might reach around $10 to $15 million. This estimate includes potential long term reputational damage and loss of trust among stakeholders.
Impact: The breach might result in the temporary suspension of research activities, delaying projects by several months. The organization could also face increased scrutiny from regulatory bodies, leading to the implementation of more stringent data security protocols.
- Compliance and Regulatory Challenges
The pharmaceutical and healthcare sectors are subject to rigorous regulatory requirements. AI systems used within these industries must comply with regulations such as the General Data Protection Regulation (GDPR) in Europe and the Food and Drug Administration (FDA) guidelines in the United States. Shadow AI, operating outside the purview of IT governance, often lacks the necessary compliance measures, putting organizations at risk of regulatory penalties.
In the pharmaceutical and healthcare sectors, Gen AI systems must adhere to strict regulatory requirements. If an AI tool is used without proper approval, it can lead to compliance issues. For example, if a hospital network employs an AI tool for diagnostic imaging that hasn’t undergone the necessary validation and approval processes, it could result in legal consequences.
Potential Issue: A tech savvy radiologist might introduce a Gen AI tool for analyzing patient scans without informing the IT department. If this unapproved AI tool, despite its accuracy, fails to meet regulatory standards, the hospital could face penalties. Average penalties for non compliance with FDA regulations can range from USD 1 million to USD 5 million, depending on the severity and duration of the non compliance.
These examples provide a sense of the potential financial impact and regulatory penalties associated with breaches and non compliance involving AI in healthcare, highlighting the importance of robust oversight and adherence to regulations.
The Emergence of Secret Cyborgs in Healthcare
Secret cyborgs, the blending of human expertise with covert Gen AI assistance, present another layer of complexity in the pharmaceutical and healthcare industries. Gen AI can enhance human capabilities.
1. Sharing Patient Information for AI Generated Reports
Example: A doctor uses a public Gen AI tool to help draft a detailed patient report, unknowingly inputting sensitive patient information into the system. Since the tool is not governed by the hospital’s data privacy protocols, this data could be stored or processed by third parties, leading to potential breaches of patient confidentiality. This underscores the need for a monitoring tool to track and prevent unauthorized sharing of sensitive information.
2. Using Public AI Tools for Research Analysis
Example: A researcher uploads confidential data sets to a public Gen AI tool to quickly generate analytical insights. However, the tool’s data processing practices are unclear, and the sensitive research data could be exposed to external entities. This situation highlights the importance of a monitoring tool that ensures all data interactions with AI systems are secure and comply with organizational data protection policies.
- Operational Inefficiencies and Workflow Disruptions
Example: There is a realistic possibility that secret AI tools, introduced without proper oversight, could lead to operational inefficiencies and workflow disruptions. For example, an unapproved AI tool integrated into a research lab might optimize certain processes but fail to align with existing data management systems, causing delays and communication breakdowns.
- Chances of Happening: As AI becomes more prevalent, the risk of such inefficiencies increases, especially if AI tools are not properly integrated or documented within an organization’s workflow.
- Shadow AI Prevention Tool: To prevent such disruptions, organizations can utilize AI governance platforms like Altimet Security that ensure all AI tools are properly integrated and aligned with existing workflows. These platforms can provide a centralized overview of all AI activities, helping to prevent miscommunications and ensuring smooth operations across teams.
Mitigating the Risks of Shadow AI and Secret Cyborgs
The challenges posed by Shadow AI and secret cyborgs in the pharmaceutical and healthcare sectors are significant but not insurmountable. Organizations can take several proactive steps to mitigate these risks and harness the benefits of AI while maintaining ethical standards, transparency, and operational efficiency.
- Strengthening IT Governance and Oversight
To combat the rise of Shadow AI, organizations must strengthen their IT governance frameworks. This includes implementing robust AI oversight mechanisms, conducting regular audits of AI systems, and ensuring that all AI tools used within the organization are approved and monitored by IT departments.
Strategy Example: Comprehensive AI Governance Framework
- Promoting Transparency and Disclosure
To address the ethical concerns associated with secret cyborgs, healthcare organizations should promote transparency and disclosure regarding the use of AI in patient care and research. This includes informing patients when AI tools are used in their diagnosis or treatment and ensuring that AI assisted decisions are clearly documented.
- Investing in Continuous Training and Skill Development
To prevent skill degradation among healthcare professionals, organizations should invest in continuous training and skill development programs. These programs should emphasize the importance of maintaining core competencies while leveraging AI as a tool to enhance, rather than replace, human expertise.
Tools to Prevent Shadow AI in Pharma and Healthcare
Preventing Shadow AI from taking root in the pharmaceutical and healthcare sectors requires a proactive approach, supported by robust tools and technologies that can monitor, manage, and mitigate unauthorized AI usage. Below are some of the key tools and strategies that organizations can leverage to prevent Shadow AI.
- AI Governance Platforms
AI governance platforms are designed to provide comprehensive oversight and management of AI systems across an organization. These platforms help ensure that all AI models, applications, and tools are tracked, validated, and compliant with internal policies and external regulations.
Key Features:
Model Inventory Management: AI governance platforms maintain a centralized inventory of all AI models in use within the organization, providing visibility into where and how AI is being applied.
Compliance Monitoring: These platforms automatically monitor AI systems for compliance with relevant regulations and internal policies, alerting stakeholders to potential risks.
Audit Trails: AI Governance tools create detailed audit trails that track changes, usage, and decision making processes, ensuring transparency and accountability.
- AI Model Validation Tools
AI model validation tools are used to assess the performance, fairness, and compliance of AI models before they are deployed. These tools help organizations ensure that any AI systems being introduced, whether officially or covertly, meet the required standards.
Key Features:
Performance Testing: These tools rigorously test AI models for accuracy, efficiency, and robustness, ensuring they perform as expected in real world scenarios.
Bias Detection: AI model validation tools assess models for potential biases, helping organizations prevent biased or unethical AI from being deployed.
Regulatory Compliance: These tools check AI models against regulatory requirements, ensuring that any AI systems used are compliant with industry standards.
- Data Loss Prevention (DLP) Tools
Data Loss Prevention (DLP) tools are essential for safeguarding sensitive data, particularly in regulated industries like pharma and healthcare. These tools help prevent unauthorized AI tools from accessing or processing sensitive data, thus mitigating the risk of Shadow AI.
Key Features:
Data Monitoring: DLP tools continuously monitor data flows within the organization to detect unauthorized access or usage by AI systems.
Policy Enforcement: These tools enforce data protection policies, preventing unauthorized data processing.
Real Time Alerts: DLP tools provide real time alerts to IT and security teams when potential data breaches or unauthorized access attempts are detected.
Check out Altimet Security’s Shadow AI prevention tool
- AI Ethics and Compliance Tools
AI ethics and compliance tools are designed to ensure that AI systems align with ethical standards and regulatory requirements. These tools can be used to evaluate both approved and unapproved AI tools, helping organizations prevent the deployment of Shadow AI that might compromise ethical guidelines.
Key Features:
Ethical Risk Assessment: These tools assess AI systems for potential ethical risks, such as bias, discrimination, or privacy violations.
Compliance Checklists: AI ethics tools provide checklists and guidelines to ensure that AI systems comply with industry regulations and ethical standards.
Impact Analysis: These tools evaluate the potential impact of AI systems on various stakeholders, ensuring that AI deployments are aligned with organizational values and societal expectations.
Integrating Tools for a Comprehensive Defense Against Shadow AI
To effectively prevent Shadow AI, organizations in the pharmaceutical and healthcare sectors should adopt a multi layered approach that integrates these tools into a cohesive AI governance strategy. By leveraging AI governance platforms, model validation tools, DLP solutions, and AI ethics tools, organizations can create a robust defense against the unauthorized deployment and use of AI systems.
Conclusion
The challenges posed by Shadow AI and secret cyborgs in the pharmaceutical and healthcare industries are significant, but they can be effectively managed through the adoption of appropriate tools and strategies. By strengthening IT governance, promoting transparency, investing in continuous training, and ensuring the integration of AI tools with existing workflows, organizations can harness the power of AI while minimizing risks.
As AI continues to revolutionize the pharma and healthcare sectors, proactive measures and the right technological tools are essential to ensure that innovation does not come at the cost of compliance, security, or ethical integrity. By addressing these challenges head on, organizations can unlock the full potential of AI, delivering better outcomes for patients, researchers, and society as a whole.
