Data Market
Why do Government and Defense Agencies need Synthetic Data? : For safety (11/23)
Keywords: Defense Agencies
Table of Contents
Introduction
With the advancement of artificial intelligence (AI), government and defense agencies are increasingly incorporating AI to detect anomalies, enhance decision-making, and improve operational efficiency. However, the integration of AI in these sectors has brought forth significant challenges, particularly concerning the security of sensitive internal data.
Adoption of AI in Government and Defense: Opportunities and Security Risks
- Adoption of AI by Government and Defense Agencies: As AI technology evolves, government and defense agencies are recognizing its potential to optimize their operations and enhance decision-making processes. AI is being utilized to identify abnormal situations, streamline processes, and boost efficiency.
- Security Risks Associated with AI: Despite its advantages, AI systems are not immune to malicious attacks. There have been instances where sensitive information was leaked due to external attacks on AI systems.
- Sensitive Data Handling: Government and defense agencies deal with highly sensitive data, often relating to national security. The leakage of such information due to malicious attacks could result in severe consequences.
Challenges in Implementing AI for Government and Defense Agencies
When government and defense agencies implement AI, several issues may arise:
- Data Security Issues: AI systems require vast amounts of data for their construction. Ensuring the confidentiality and security of this data is paramount. A data breach or hack could pose a significant threat to national security.
- Accuracy and Reliability: AI predictions and analyses depend heavily on data accuracy. Utilizing incorrect or biased data could lead to distorted outcomes, negatively affecting crucial decision-making processes.
- Ethical Concerns: In scenarios where AI’s decisions directly affect human lives, ethical dilemmas and issues may emerge. For instance, in the case of unmanned drones conducting military operations, misidentification by AI could lead to substantial human casualties.
- Infrastructure and Technical Deficiencies: Effective AI system operation requires advanced technology and infrastructure. Some governmental agencies may lack these resources, hindering AI adoption.
- Cost Issues: Building and maintaining AI systems are costly endeavors. Budget constraints might pose challenges to the sustainable operation of long-term AI projects.
Advantages of Using Synthetic Data for Government and Defense Agencies
Furthermore, meaningful data sharing or utilization outside the organization can be limited, impeding technological advancement. These data issues can be addressed through the generation of synthetic data.
Benefits of Synthetic Data
- Resolution of Data Security Issues: Synthetic data does not contain real personal or sensitive information, thus resolving data security issues. This is particularly valuable in adhering to restrictions on accessing civilian data.
- Overcoming Data Bias: Real data often carries biases. For example, medical data may predominantly represent white male information. Synthetic data can generate diverse datasets to overcome such biases.
- Data Volume Augmentation: Collecting real data is time-consuming and expensive. Synthetic data can be generated using AI to significantly increase data volumes, which is crucial for securing learning data in the defense sector.
- Ensuring Data Diversity: The defense sector requires diverse datasets to prepare for various environments and situations. Synthetic data can simulate different scenarios and environments, providing data diversity similar to real data.
- Ease of Data Management: Managing synthetic data is straightforward as it does not demand extensive labeling and tagging costs. This is particularly beneficial for methodically managing and utilizing data within defense agencies.
- Resolution of Regulatory and Legal Issues: Synthetic data does not contain personal or sensitive information, obviating the need for user consent and freeing it from regulatory constraints. This assists government and defense agencies in addressing legal issues related to data utilization.
- Enhancement of AI Model Performance: Given that synthetic data is created using the statistical properties and structures of original data, it aids in improving AI model performance. This is critically important in boosting AI model accuracy in the defense sector.
Ensuring Security in Synthetic Data Generation with Cubig’s DTS
Traditional synthetic data generation techniques are vulnerable to external attacks and have a high likelihood of being reconstructed back to the original data. Moreover, generating synthetic data typically requires the original data to be uploaded to external servers, posing a risk of exposing sensitive personal information.
Cubig’s DTS, however, applies data non-access techniques and differential privacy protection technologies to create secure synthetic data. This method allows for the generation of synthetic data with up to 99% performance similarity to the original data without accessing the original data, ensuring both security and efficacy.
If you’re interested in learning more about DTS and the company that developed it, CUBIG, please visit the link below.
CUBIG Link: CUBIG
Blog Link: Blog
