Trust in AI deep dive:Secure and Ethical
As AI technology becomes more widespread, there is a growing need to prepare for vulnerabilities, cyber threats, and ethical risks. Ensuring security and ethics is a critical factor for companies to earn society’s trust and achieve sustainable growth.
Featured Content: A Journey Through History—Learning from the Past
Events that marked turning points in history raise questions that still resonate with today’s business leaders: the heliocentric theory, the Renaissance, the Titanic, the Silk Road, and the Age of Discovery. This series uses stories from the past as clues to understand the meaning of “trust” in the age of AI.
“Multi-AI Agent Technology,” in which AI systems collaborate like a team to carry out tasks
Technology introduction
Multi-AI agent technology enables multiple AI agents to work together like a team while continuously and safely learning from execution results, human feedback, policy updates, and specification changes. The agents identify why actions succeeded or failed, extract knowledge and best practices, and apply validated learnings to future tasks.
Why it matters
This technology reduces the need for ongoing expert-led tuning by enabling AI to continuously evolve within business operations. Organizations can build and improve AI tailored to their business needs without heavy dependence on specialized AI expertise.
Example use case
Making vast information easier to discover
In business systems such as electronic medical records, identifying the impact of regulatory or policy changes often depends on experienced specialists who know where to look across large volumes of documentation. By applying Multi-AI Agent Technology, AI agents learn from previous search results and human feedback to identify not only directly related documents but also relevant supporting information. This reduces manual effort while enabling more comprehensive and reliable impact analysis.
“AI Security Technology” for the safe and continuous use of AI
Technology introduction
Multiple AI systems can collaborate across attack, defense, and validation functions to identify vulnerabilities, analyze potential attack patterns, assess countermeasure effectiveness, and address emerging threats such as prompt injection attacks. Additionally, for generative AI, there is a mechanism that uses attack prompts to inspect for vulnerabilities and automatically generates and applies corresponding guardrails. This makes it possible to identify potential risks inherent in the AI itself in advance and continuously enhance defensive capabilities.
