Cybersecurity threats have evolved dramatically in recent years, with global events enabling adversaries to leverage misinformation, public uncertainty, and a growing reliance on digital transformation. Fast forward to today, and we’re seeing artificial intelligence (AI) and commercially available information (CAI) reshape the digital security landscape once again.
One major shift is the sheer volume of data available to organizations. Public and private sector entities are data rich, whether that data is generated internally, purchased from CAI vendors, or acquired through other means.
Data abundance can help fuel smarter decisions and drive innovations like large language model (LLM) development. However, allowing this information to be accessed, shared, and used—both internally and externally—without the right safeguards can expose your organization to serious security risks. To avoid the legal consequences, financial penalties, and reputation damage associated with data leakage, it’s critical to ensure that:
- Users can only access and share data that they’re authorized to handle.
- Sensitive information isn’t unnecessarily used for applications like AI development.
- Outdated or manual data management processes aren’t causing sensitive data exposure.
While these requirements can be difficult to meet with many existing data governance solutions, ORIGIN is designed to protect sensitive data and enforce access controls at scale without compromising data accessibility and collaboration.
How data sharing exposes your organization
As your organization gathers, stores, and shares data—potentially among a large and distributed user base—the risk of a security incident is impacted by three major factors: sensitive data oversight, vulnerable access controls, and the emergence of AI.
The hidden dangers of sensitive information
Many organizations unknowingly store, share, and process sensitive data, such as personally identifiable information (PII)—especially those that purchase data from third-party vendors. Managing sensitive citizen data, intelligence, and other vulnerable content is also common in government agencies and other specific sectors.
While sensitive information is more readily available than ever in the age of CAI, it’s often challenging to pinpoint and control within large, evolving datasets moving across (or outside of) your organization. Sensitive data definitions and management standards also vary by region and industry, creating the potential for further confusion and oversight.
Weak access controls and data leakage
Without strong data access controls, users may encounter sensitive information they aren’t authorized to use, or simply don’t need. This information then risks exposure, whether or not the user has malicious intent. In some cases, organizations deliberately relax access to promote collaboration: after 9/11, the United States government broadened classified data permissions to encourage intelligence sharing. This approach ultimately breached the Pentagon’s security.
In both public and private sectors, organizations often lack the tools and strategies needed to balance data accessibility with security. Manual processes like risk assessments and rewriting documents on a case-by-case basis are common for finding and correcting access control violations. Developers may need to query, cleanse, and transfer data manually anytime it’s requested from users to mitigate security issues, for example. This approach is laborious, costly, and error-prone.
AI-specific security threats
Many organizations are developing AI applications, but allowing black box AI systems to ingest sensitive information can introduce security threats. For example, adversaries may use techniques like prompt injection and jailbreaking to extract LLM training data. Ideally, sensitive information is cleansed from training datasets before being used to develop AI, though many organizations lack the tools or understanding to enforce such policies at scale.
How ORIGIN secures your data pipelines
With these growing risks, organizations need a smarter, more automated way to manage and protect their data—one that doesn’t rely on time-consuming processes or promote collaboration at the cost of security.
To meet this need, Intlabs developed ORIGIN: a data governance platform that allows users to access and share the data they need while keeping all data interactions secure, auditable, and compliant with data laws and policies. It addresses common security risks associated with data sharing through four capabilities:
1. Sanitize sensitive data at scale
One of ORIGIN’s core functions is the ability to redact or cleanse sensitive information that would otherwise violate data laws and privacy policies if shared or processed. To avoid manual data sensitivity audits, ORIGIN uses AI to compare datasets against specific laws and policies, automatically pinpointing non-compliant categories of information. Then, it suggests actions, like redaction, to safeguard this data before it’s transferred or used.
For example, imagine that your organization is using large commercial datasets and internal documents to fine-tune an LLM. You can add these datasets to ORIGIN and locate and remove sensitive data—anything you don’t want the LLM ingesting—before sharing those datasets with developers. This is an efficient and scalable approach to data minimization, which helps prevent data leakage through AI systems.
2. Fine-tune your access controls
ORIGIN follows a zero-trust approach, meaning that no user has data access by default. Instead, administrators precisely control who can view, change, or share datasets by specific individuals or user groups. These access controls extend beyond user roles, allowing you to limit access by user location. This is often important for enhanced security when operating across jurisdictions, especially if those jurisdictions are governed by different data protection laws.
3. Share securely in a data mesh
ORIGIN uses a data mesh, a decentralized data management technique. Generally speaking, a data mesh assigns data ownership to respective teams and domains—as opposed to managing information under one team or storage location. A data mesh offers greater efficiency, scalability, and data quality than centralized ownership.
ORIGIN is a mesh in the sense that it gives users a single point of access to varied data sources. These sources may originate from different servers, include different data types, and have different access permissions.
ORIGIN’s data cleansing and access control mechanisms ensure that this distributed data network follows consistent security standards, like redacting PII. Additionally, authorized users can easily access the data they need without needlessly moving it around or interacting directly with the original data repositories—actions that can weaken security.
4. Reliably record every data interaction
ORIGIN creates a record of every data activity, including when, how, and by whom data is handled. This transparency allows organizations to detect security risks, such as unauthorized data manipulation or access. It also makes it easy to perform data audits, reports, or investigations, which are often required to follow security best practices and comply with data protection laws.
ORIGIN’s responsible approach to AI
Because ORIGIN uses AI, we’d be remiss not to address the security risks of including this technology in your data governance strategy. Unlike many other AI-driven platforms, however, ORIGIN does not use your data to train its sensitivity analysis tool. Instead, ORIGIN’s AI is trained solely on relevant data laws and policies and does not use any of your information to inform its outputs.
Secondly, it’s important to note that while ORIGIN uses AI to analyze data sensitivity and suggest how to protect it, the model does not implement actions like data redaction autonomously. Instead, data cleansing is accomplished through consistent, accurate, and predictable algorithms. This prevents AI overreliance, which can generate inaccurate results and potentially expose rather than protect sensitive data.
The bottom line
Organizations can no longer afford to rely on outdated, manual processes to protect the sensitive information flowing through their data pipelines. From regulatory compliance to AI-driven security risks, the challenges of data sharing require modern, innovative solutions.
ORIGIN provides a secure, automated, and scalable approach to data sharing, empowering your organization to:
- Prevent data leaks that could lead to financial penalties, legal repercussions, and reputation damage.
- Save costs by using more efficient access control and data cleansing techniques.
- Stay ahead of evolving data protection laws and security standards with an adaptive, AI-powered solution.
- Improve data accessibility and collaboration without exposing vulnerable information.
- Establish trust with users, customers, stakeholders, and regulators.