AI Security and Compliance in Pharmaceuticals
As the Head of Compliance at a leading pharmaceuticals company, you understand the critical need for maintaining trust and security in the rapidly evolving landscape of AI and machine learning. With the increasing reliance on AI systems in medical applications, the potential threats posed by adversarial attacks on medical machine learning are a significant concern. The emergence of agentic AI has only heightened the complexity and risks associated with safeguarding AI trust and security. Trustwise recognizes this challenge and offers a comprehensive AI Security and Control Layer, with a specific focus on AI Trust Management for Agentic AI Systems. Our solutions, embodied in Harmony Ai, are designed to minimize the Trust Gap throughout the entire AI lifecycle, from simulation and verification to optimization and governance.
The Trust Gap: A Barrier to Widespread AI Adoption
Trustwise acknowledges that modern AI projects often encounter barriers to scaling, not due to a lack of ambition, but as a result of unreliability, inefficiency, and a lack of control. This is the Trust Gap – a critical barrier to achieving widespread AI adoption. The emergence of agentic AI only widens this gap, introducing greater complexity and risk. In this context, the need for reliable and secure AI systems in medical applications cannot be overstated. Trustwise’s approach centers on embedding real-time security, control, and alignment into every agent, enabling innovation to scale without compromising control. We transform naked agents into Shielded Agents, ensuring that trust and security are integral components of every AI system.
Real-time Security and Control
Trustwise delivers trust-as-code through APIs, SDKs, MCPs, and Guardian Agents, providing tailored solutions to suit the specific needs of your organization. With real-time security and control, you can mitigate the risks associated with adversarial attacks on medical machine learning and ensure the integrity and reliability of AI systems in the pharmaceutical industry. Our approach empowers large organizations to realize AI Trust and Security at scale, enabling you to proactively address potential threats and vulnerabilities in AI systems.
Minimizing the Risks of Adversarial Attacks
Adversarial attacks on medical machine learning pose a significant threat to the integrity and reliability of AI systems in the healthcare sector. With the potential for malicious, drifted, or poisoned tools to infiltrate AI systems, the consequences of compromised trust and security can be severe. Trustwise’s comprehensive AI Security and Control Layer, coupled with our AI Trust Management for Agentic AI Systems, offers a proactive and robust defense against adversarial attacks. By minimizing the Trust Gap and embedding real-time security and control into AI systems, our solutions provide the necessary safeguards to protect sensitive medical data and ensure the ethical deployment of AI in healthcare.
Safeguarding AI Trust and Security
In the corporate industry, where inadequate visibility and control over AI systems can have far-reaching implications, the need for robust AI trust and security measures cannot be overstated. Trustwise’s approach is tailored to address the specific challenges faced by large organizations, providing the tools and frameworks necessary to safeguard AI trust and security in multi-cloud or partner-integrated environments. By empowering executives with the ability to maintain control over potentially malicious, drifted, or poisoned tools, Trustwise ensures that AI systems in the pharmaceutical industry adhere to the highest standards of trust and security.
Schedule Demo
To experience firsthand how Trustwise’s AI Security and Control Layer can enhance the trust and security of your AI systems in the pharmaceutical industry, we invite you to schedule a demo with our team. Gain valuable insights into how our solutions can minimize the Trust Gap, mitigate the risks of adversarial attacks, and empower your organization to realize AI Trust and Security at scale.
