Opportunity: Unique Entity-Bound Secure Database
Entity-bound secure databases offer an advanced approach to data protection, where access and permissions are tied directly to individual users or entities. This innovative solution helps organizations safeguard sensitive information across various applications, ensuring robust security measures are in place.
Dataparency® offers a Secure Entity-bound Database technology within a Distributed Data Network, using Relationship Distributed Identifiers (RDID) to define entity relationships, our patented technology for securing data access, described in later slides.
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The Three Uses of Data
1
Data as Security
- Access Control
- Identity Management
- 5G/Network Configuration
- Endpoint Management
- IoT Device Management
- System Log aggregation
2
Data as Value
- Enterprise Data Streams Pipeline
- Production Process Output
- Web Services
- IoT Device Data Streams
- System Log Data Streams
3
Data as Control
- Program Execution Control
- Program Control Group
- Web Services
- Browser Control
- Gateway Services
Importance of Data Security in Applications
1
Safeguard Sensitive Data
Protect critical information, such as personal records, financial data, and intellectual property, from unauthorized access or breaches.
2
Compliance and Regulations
Adhere to industry-specific data privacy laws and regulations, mitigating the risk of costly fines and legal repercussions.
3
Maintain Customer Trust
Data breaches can permanently damage customer trust and future long-term relationships and are very costly to business operations. Customers may not come back if they know you've lost personal data about them.
Challenges with Traditional Database Security
Rigid Access Controls
Traditional database security often relies on inflexible user roles and permissions, struggling to adapt to evolving security needs and dynamic access requirements.
Centralized Management
The centralized nature of traditional database security can create bottlenecks and single points of failure, compromising the overall resilience and responsiveness of the system.
Lack of Granularity
Coarse-grained access controls in traditional databases may not provide the necessary level of granularity required to effectively secure sensitive data and ensure appropriate access privileges. Non-integrated controls.
Key Features of Entity-Bound Secure Database 1
What We Do That They Cannot
Encryption and Data Protection
- Us→ Advanced, per entity, aes256 encryption and data redaction ensure the confidentiality and integrity of sensitive information. All driven within the standard processing of Dataparency's Database technology. Loss of encryption key allows only a single entity's data to be discovered. And even then they must know the 'path' and location components used by the hashing routine under which the data was stored to retrieve the data. Exfiltration of database cannot be used.
- Them→ Single encryption/signing key to encrypt the entire dataset. If stolen the entire dataset is lost. Data schema is open to inspection. Easy to find data with SQL SELECT * statement. SQL injection attacks allow unrestricted access to all data. Number one hack attack and responsible for most major breaches.
Granular Access Control
- Us→ Permissions are controlled by a Relationship Distributed IDentifier (RDID) defining the relationship with the entity whose data is desired, at an individual entity level, allowing for precise control over data access and usage. Permissions allow access to be restricted at a document and/or document attribute level.
- Them → Others can only secure their Database at the global level leaving security at the allow/disallow choice. Not very effective and prone to hacking when the database password has been compromised due to a hack or a wrong configuration as with no password at all. And user permissions cannot be assigned to document-level access much less at an document attribute level.
Key Features of Entity-Bound Secure Database 2
What We Do That They Cannot
Networking/Data Sharing/Marketplaces
- Us→ We offer a complete Distributed Data Network (Dataparency DDN) out of the box. Using the inbuilt Open Source NATS messaging network, we can place data storage locations to comply with privacy regulations like GDPR, by entity, simplifying operations. Dataparency DDN provides the ability to share data between trading partners in a secure and access-controlled way. Put your data ‘in the network’ where it can be accessed by others in a controlled way. Build marketplaces controlled by a consortium of trading partners.
- Them→ Sharing? What's sharing? Other cannot be externally shared. They have no capacity to allow external access to the data without programming.
Networking - continued
- Us→ DDN provides automatic backup, geolocation, horizontally scalable, and encrypted data movement, from start to finish. Scale services to your needs without buying additional software. And the security is present at each node of the network…automatically without additional programming. Zero-Trust is a built-in capability at every step, from the ground up, automatically.
- Them → Huh? Others cannot provide the scalable solution that we can. They don't have the features offered by DDN. They are hard if not impossible to manage in a Zero-Trust environment without excessive programming effort, security of which is questionable at best and must meet strict procedures, that most enterprises don't have.
Key Features of Entity-Bound Secure Database 3
What We Do That They Cannot
Additional Features Missing in Others
- Secure Operational Data
- Automatic Provisioning of Entities
- Bring Legacy Application Forward into Security
- AI and Data Control
- AI-Assisted Domain-Driven-Design (DDD)
- Manufacturing 4.0 and IoT Device Endpoints
Visit Our Website for Additional Information
How it Works
Identity Based Security
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Distributed Security Management
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Adaptive Security: Safeguarding Your Data
View more
Use Cases and Benefits
Healthcare
Secure sensitive patient data, comply with regulations, and maintain patient trust.
Finance
Protect financial records, transactions, and customer information from unauthorized access.
Government
Safeguard classified information, enable data sharing while maintaining strict security controls.
Education
Secure student records, research data, and intellectual property within educational institutions.
Financial Model
1
2
3
4
1
Applications
2
Frameworks
3
D-DDN
4
D-ISP
Each layer builds upon the previous layer.
Each layer builds upon the lower layer. Each layer builds upon relationships in lower layer. Each relationship is an income producing item.
The number of relationships controlled provides the recurring income stream that will make Dataparency profitable.
Sales by License Type
Implementation Considerations
Where we'll spend the investment
1
Build Out Core
Refactor existing service code to harden security, provide network error recovery, and increase source code maintainability.
2
Integration & Applications
Build seamless integration of the entity-bound secure database with existing/new applications; data, security, infrastructure, ETL/ELT pipelines, and workflows, etc.
3
Deployment
Carefully plan and execute the marketing, distribution, and deployment process. Minimize disruptions and ensure a smooth transition to our new security system.
Initial Application Product Targets
Private Secure Data Exchange
Securely exchange sensitive data, while complying with regulations, and maintain user trust with CloakFS-based web app. Virtually hack-proof data movement.
Education Students Data Sharing Network
Secure student records, research data, and intellectual property within educational institutions.
Government Data Sharing Networks
Safeguard classified information, enable data sharing while maintaining strict security controls. Use DDN, CloakFS, and OTP (One Time Pad) technologies.
Future Applications
Many Distributed Data Network (DDN) Applications to follow …
Future Research into Product Targets
GenAI and AI Data Integration
The GenAI field is seeing more and more, the need for quality data to train models. Enterprise data is the most valuable but is proprietary and privacy must be maintained. We can offer such data privately through our Distributed Data Network. And data sharing could build Data Marketplaces where enterprise data can be monetized and shared in a controlled, secure way.
AI Prompting Storage and Playback
With our schema-less storage capability, and Python compatibility, we can store AI prompting for future playback and development. Build a Vector database wrapper to service AI model searches. Build an AI data source attribution and authorization tech to allow models to comply with content ownership and attribution issues.
Future AI Applications
Many AI technologies to follow …
Conclusion and Future Outlook
Contact
Email us with inquiries & investment opportunities: [email protected]
Phone: CEO Timothy V. Dix (719) 210-5318 (cell)
