Understanding the Bigger Picture
Imagine you're presented with three seemingly unrelated pieces of information:
- A customer database contains credit card information.
- The data science team operates out of Singapore.
- Developers have been tasked with experimenting with generative AI.
Individually, these facts don't mean much. But what if you discovered that the customer database, which includes credit card information, isn’t encrypted? Moreover, this database contains data of EU residents, which is accessible by the data science team in Singapore. To complicate matters, this same team is using this sensitive data in a generative AI application to derive inferences. Learn more about the specific challenges posed by unstructured data in GenAI.
Suddenly, these isolated pieces of information tell a very different story—one that raises significant concerns. You now realize that sensitive data is not only unprotected but is also being used in a way that violates cross-border data transfer laws and GDPR regulations. This is the power of data context—it transforms random facts into actionable insights that drive critical decisions to secure and ensure the privacy of your data.
Ready to protect your sensitive data with Contextual Data+AI Intelligence? Download our white paper or request a demo now to see how you can enable safe use of data+AI.
The Challenge of Finding Data Context
Gaining meaningful context around your data is no easy task. The information you need to make informed decisions about data security, privacy and compliance is often scattered across hundreds of different tools and locked in the minds of various team members. Integrating these tools and aligning these teams is a daunting challenge, but the lack of context can have catastrophic consequences for your organization.
The Need for Speed
In today’s fast-paced world, how quickly you respond to a data security incident can be crucial. Regulatory bodies, including the SEC, increasingly require companies to disclose material cybersecurity incidents. Determining whether an incident is 'material' involves gathering extensive information: Has sensitive data been exposed? How much data is exposed? Whose data is it? What laws have been violated? Is the scale of the data breach significant enough to cause material damage to the company, shareholders, or the affected individuals? For example, under the GDPR, personal data breaches must be reported within 72 hours of awareness.
Imagine the relief of knowing your data is protected and compliant with regulations—no matter where it’s stored or how it’s used. Download our white paper or request a demo now to discover how contextual data+AI intelligence can give you peace of mind.
Collecting and correlating all this information, especially when it’s dispersed across various tools and playbooks, can be time-consuming and impede your ability to respond effectively to a breach.
Solving Complex Data Problems
Identifying the presence of sensitive data is valuable, but it’s only the first step in solving real-world problems. Today’s organizations face challenges ranging from preventing cross-border data transfers to ensuring sensitive data isn’t misused in generative AI applications in violation of customer contracts, company policies and regulatory laws. Addressing these issues requires collecting and integrating insights from multiple sources—a task that’s far from straightforward.
The Problem of Siloed Data Teams
In most organizations, multiple stakeholders are responsible for data: data security, privacy, governance, and even data user teams, among others. Each team looks at data through its own lens, creating a fragmented view of the data landscape. Aligning these teams on a common source of truth is critical but rarely the reality. This lack of alignment can lead to inconsistent data handling practices, eroding customer trust and increasing the risk of data breaches.
The Power of Contextual Data+AI Intelligence: The Securiti Data Command Graph
Enter the Securiti Data Command Graph—a unique knowledge graph that provides full contextual intelligence about all your data and AI assets across hybrid, multi-cloud environments. Think of it as a "Google search" for your data and AI. It allows you to ask any question about your data+AI assets, correlating metadata from various perspectives, including data security, privacy, governance, compliance, and data users.
With this knowledge graph, you can derive granular insights down to the level of a file, table, or column, enabling you to solve complex data problems. For instance, in the scenario mentioned earlier, a single click into the AI model would reveal the different data sources, the types of sensitive data they contain, which users have access to that data, and what entitlements they have through the generative AI application. You can also view any security and privacy issues and the controls in place to ensure compliance—all in one comprehensive view.
