CHOROLOGY.ai has emerged from stealth mode with a focus on applying generative artificial intelligence (AI) to enhance data governance. The company has developed a specialized language model tailored to discover various data types, simplifying compliance, ensuring data privacy, and enhancing security posture enforcement.
CEO/CTO Tarique Mustafa highlighted that their auto-data discovery engine, coupled with auto-data classification and mapping engines, eliminates the need for pre-processing data to enforce governance and compliance policies. This capability collectively allows organizations to significantly reduce the total cost of data governance.
Moreover, the domain-specific language model enables cybersecurity and IT teams to utilize natural language queries, reducing the expertise required to govern data effectively. Mustafa emphasized that this approach will democratize compliance, making it easier for organizations of all sizes to meet diverse regulatory mandates.
With the advent of stringent data privacy regulations like the California Consumer Privacy Act of 2018 (CCPA), Mustafa underscored the necessity for organizations to adopt more efficient data governance strategies. Rather than relying on large language models (LLMs), CHOROLOGY.ai chose to develop a domain-specific model tailored specifically for data governance. This model is extended through multiple engines for data discovery, classification, and mapping.
The transformative potential of AI in data security is undeniable. CHOROLOGY.ai anticipates that organizations managing sensitive data will prefer domain-specific models trained explicitly for data management. As data volumes continue to grow exponentially, effective data governance and security will increasingly rely on AI technologies. However, the company also acknowledges the looming threat of cybercriminals leveraging AI for malicious purposes.
The challenge lies in securing the necessary funding to deploy AI technologies across organizations for real-time data governance and security. While it may take years for all platforms and tools to integrate AI capabilities fully, organizations can rationalize costs by prioritizing AI-infused upgrades and adjusting spending on perimeter-focused security solutions. As applications evolve, traditional security paradigms are evolving, necessitating a shift towards AI-driven data governance controls.
In conclusion, the future of data security hinges on effectively harnessing AI. The ongoing challenge is to determine the most efficient and impactful ways to apply AI technologies to manage and secure data effectively in a rapidly evolving digital landscape.
Source: securityboulevard.com
