CHOROLOGY.ai today emerged from stealth to apply a domain-specific artificial intelligence (AI) model to data governance.
The company has developed a domain-specific language model capable of discovering all the types of data to make it simpler to achieve compliance, ensure data privacy and improve security posture enforcement.
Using an auto-data discovery engine coupled with an auto-data classification and an auto-data mapping engine the domain-specific language model eliminates the need to pre-process data to apply governance and compliance policies.
Company CEO/CTO Tarique Mustafa said those capabilities collectively enable organizations to significantly reduce the total cost of data governance.
At the same time, the domain-specific language model also enables cybersecurity and IT teams to leverage natural language queries in a way that reduces the overall level of expertise required to govern data, he added. That approach, in effect, will make it easier for organizations of all sizes to meet a wide range of compliance mandates, noted Mustafa.
As more data privacy regulations such as the California Consumer Privacy Act of 2018 (CCPA) go into effect, just about every organization will need to find a more efficient approach to governing data, said Mustafa. Rather than trying to leverage a large language model (LLM) to achieve that goal, CHOROLOGY.ai opted to build a domain-specific language model for governing data that it then extends using multiple engines for discovery, classification and mapping, he explained.
AI Will Transform Data Security
There is little doubt that generative AI will eventually transform how data is governed and secured, but exactly how that will be accomplished isn’t entirely clear. CHOROLOGY.ai is betting that given the sensitivity of the data being governed organizations will prefer to rely on a domain-specific language model specifically trained to manage data.
Regardless of approach, it’s clear the amount of data being created every day exceeds the ability of most organizations to effectively manage it. The only way to cost-effectively govern and secure data will be to rely more on various forms of AI. Unfortunately, organizations should also assume that cybercriminals will also use AI in the near future to discover where their most sensitive data can be most easily found. The only way to effectively thwart those efforts will be to apply data governance controls as data in near real-time as data is being created.
The challenge is finding the funding needed to acquire the various AI technologies required to achieve that goal. It might be several years before all the tools and platforms used by an organization to secure data are infused with AI capabilities. Many organizations, however, might be able to cost-justify those upgrades by rationalizing existing tools and platforms, or by spending less on platforms that primarily secure network perimeters — that as applications continue to evolve are increasingly dissolving.
No matter how data security evolves there will never be enough expertise available to manage it without the aid of AI. All that remains to be resolved now is how to effectively apply it.
