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AI Security Policy

Givzey leverages artificial intelligence (AI) to enable our company to deliver smarter, more efficient, and highly personalized services, ultimately enhancing end user experience and driving innovation.

Objective

The purpose of this Artificial Intelligence and Machine Learning policy is to establish clear guidelines and ethical standards for the development, deployment, and management of artificial intelligence and machine learning technologies within our SaaS applications. This policy aims to:

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1. Ensure Ethical Use: Promote responsible and fair use of AI/ML technologies by adhering to principles of transparency, accountability, and respect for user privacy.
 

2. Enhance User Experience: Leverage AI/ML to provide innovative, personalized, and efficient solutions that enhance the overall user experience and value of our SaaS application.
 

3. Maintain Data Security: Safeguard user data and ensure robust security measures are in place to protect against unauthorized access, misuse, or breaches.
 

4. Promote Transparency: Clearly communicate how AI/ML technologies are utilized within the application, including their impact on user interactions and decision-making processes.
 

5. Foster Continuous Improvement: Encourage ongoing evaluation and refinement of AI/ML models to improve accuracy, fairness, and effectiveness, while addressing potential biases and ethical concerns.
 

6. Ensure Compliance: Adhere to relevant legal and regulatory requirements governing AI/ML use, data protection, and privacy, and align with industry best practices.

 

By adhering to this policy, we aim to build trust with our users, drive innovation responsibly, and maintain the highest standards of ethical and operational excellence in our use of AI/ML technologies. This policy intends to cover any Givzey product, including Version2, the autonomous fundraiser application.

Definitions

Artificial Intelligence (AI): 

In the context of our applications, Artificial Intelligence (AI) refers to a suite of advanced computational techniques and algorithms designed to analyze, interpret, and leverage large volumes of data to inform and optimize data modeling processes. AI encompasses machine learning (ML), natural language processing (NLP), and other data-driven methodologies that enable our system to recognize patterns, make predictions, and generate actionable insights based on historical and real-time data.


AI in our SaaS environment is employed to:
 

1. Enhance Data Accuracy: Utilize algorithms to improve the precision of data models by identifying underlying patterns and trends that may not be apparent through traditional analysis methods.
 

2. Automate Decision-Making: Enable automated decision-making processes by deriving insights and recommendations from data, thereby streamlining operations and reducing manual intervention.
 

3. Personalize User Experience: Tailor the user experience by analyzing individual user behavior and preferences, resulting in more relevant and engaging interactions.
 

4. Predict Future Trends: Forecast future trends and outcomes based on historical data, helping users make informed decisions and plan proactively.
 

5. Optimize Performance: Continuously refine and optimize data models to enhance their effectiveness and adapt to evolving data landscapes.

 

In essence, AI in our application serves as a powerful tool to transform data into strategic assets, driving innovation and providing our users with advanced analytical capabilities that enhance their overall experience and decision-making processes.

Machine Learning (ML): 

In the context of our applications, Machine Learning (ML) refers to a branch of artificial intelligence that involves the development and application of algorithms and statistical models that enable the system to automatically learn from and make predictions or decisions based on data. ML facilitates the automation of complex processes by enabling our application to adapt and improve its performance over time without explicit reprogramming.

 

Machine Learning in our SaaS environment is employed to:

1. Automate Repetitive Tasks: Streamline and automate routine and repetitive tasks, reducing the need for manual intervention and increasing operational efficiency.
 

2. Enhance Process Accuracy: Improve the accuracy of automated processes by learning from historical data and adapting to new patterns, thereby minimizing errors and inconsistencies.
 

3. Personalize Interactions: Enable dynamic personalization of user interactions by analyzing individual preferences and behaviors to deliver more relevant and customized experiences.
 

4. Optimize Resource Allocation: Analyze and predict resource needs to optimize allocation and utilization, enhancing overall system performance and cost-effectiveness.
 

5. Adapt to Changing Conditions: Continuously learn from new data and adapt to changing conditions, ensuring that automated processes remain effective and relevant as data evolves.
 

In summary, Machine Learning within our SaaS application is a key enabler of process automation, driving efficiencies and improvements by leveraging data-driven insights to perform tasks and make decisions with increasing precision and relevance.

Policy

Givzey adheres to the following practices in regards to the use of ML and AI in our software.

 

Fairness

Givzey is committed to designing, developing, and deploying AI systems that ensure equitable treatment and outcomes for all users. Our team employs methodology for proactive identification and mitigation of biases in AI models, data, and processes to prevent any form of discrimination or unjust treatment based on characteristics such as race, gender, age, disability, or other protected attributes.


Transparency
Givzey is dedicated to building trust-based partnerships with our clients and their donors. As part of this commitment, we pledge to maintain full transparency regarding how our products utilize AI in relation to client data. 

Data Collection
Givzey uses select data points from a client CRM, which are provided and authorized for use by the client, to inform interactions with our software. Data is transferred between the client and Givzey using a secure integration via either an authorized API or SFTP file transfer. For our technical documentation, please email hello@version2.ai and we’ll be happy to provide details on this process.

 

Transparency
The Version2 product operates on full transparency within donor communications. All communications clearly state the use of AI in the Virtual Engagement Officer, and provide the donor with alternatives. This level of communication demonstrates Givzey’s commitment to ethical practices, transparency, and respecting donor autonomy. As our clients adopt new technologies, we prioritize their donors' trust and preferences.

 

Data Storage
Version2 will collect and store the data obtained from interactions with the donor or potential donor for return to the customer CRM and to inform future interactions.

Since Version2 relies on algorithms and AI proprietary to Givzey, at no time does the client data leave the company’s secure servers. No personally identifiable information (PII) is exposed to third party vendors. Please refer to Givzey’s Data Protection and Security Policy for additional information.

 

Data Retention and Deletion
Givzey will delete, or return your data when you terminate our services in accordance with our agreements with you. All input, output and usage data is stored in a secure, encrypted, user-restricted database.

 

AI service providers that provide the technology underlying some of our AI features have their own data retention and deletion policies. For example, OpenAI retains your data for 30 days for security purposes but does not store any personal data shared during interactions long-term. Additionally, any third-party AI service providers used do not utilize your data to train their models.

 

  • Our partnered services do not train on, or take your business data\

  • Givzey, not our partnered services, own the inputs and outputs (where allowed by law)

  • Our partnered services have been audited for SOC 2 compliance

Ethical Use
At Givzey we are committed to the ethical use of artificial intelligence (AI) to ensure that our technologies are deployed in ways that align with our core values and societal norms. Our commitment to ethical use includes the following principles:
 

1. Respect for Human Rights: We design and implement AI systems that respect and uphold fundamental human rights, avoiding any actions that could infringe upon individual freedoms or personal dignity.
 

2. Integrity and Honesty: We ensure that AI technologies are used with integrity and honesty, providing clear and truthful information about how they function and the implications of their use.
 

3. Social Responsibility: We are dedicated to using AI in ways that contribute positively to society and avoid any applications that could cause harm, promote inequality, or negatively impact communities.
 

4. Consent and Privacy: We prioritize user consent and privacy by ensuring that AI systems handle personal data responsibly, with transparency about data collection, usage, and storage practices.
 

5. Accountability and Oversight: We maintain robust mechanisms for accountability and oversight to monitor the impact of AI systems, address any ethical concerns that arise, and make necessary adjustments to ensure ethical standards are upheld.
 

6. Continuous Improvement: We are committed to the continuous evaluation and improvement of our AI practices to adapt to emerging ethical considerations and technological advancements.

 

By adhering to these principles, we aim to foster trust and ensure that our AI technologies are used in a manner that is ethical, responsible, and aligned with the best interests of our clients and society.
 

Development and Deployment

Development Phase

 

1. Data Anonymization and Encryption:

  • Ensure that all sensitive data used for training AI models is anonymized to protect individual privacy.

  • Use strong encryption protocols both at rest and in transit to safeguard data from unauthorized access

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2. Secure Development Practices:

  • Follow secure coding practices to prevent vulnerabilities such as SQL injection, cross-site scripting (XSS), and others.

  • Conduct regular code reviews and use static and dynamic analysis tools to identify and rectify security issues early.

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3. Ethical AI Frameworks

  • Develop and adhere to ethical guidelines that dictate the responsible use of AI, including fairness, accountability, and transparency.

  • Implement bias detection and mitigation techniques to ensure the AI does not make unfair or discriminatory decisions.

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4. Access Control and Authentication:

  • Implement robust access control mechanisms to ensure that only authorized personnel have access to sensitive data and systems.

  • Use multi-factor authentication (MFA) for added security.

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5. Audit Trails and Logging:

  • Maintain detailed logs of all activities related to AI model training and data handling.

  • Regularly audit these logs to detect and respond to any suspicious activities.

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Deployment Phase

 

1. Environmental Hardening:

  • Ensure that the deployment environment is secured by disabling unnecessary services and ports.

  • Regularly apply security patches and updates to all software components.


2. Continuous Monitoring and Incident Response:

  • Set up continuous monitoring of AI and ML systems to detect anomalies and potential security breaches in real-time.

  • Develop and maintain a robust incident response plan to address security incidents promptly and effectively.

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3. Data Governance:

  • Implement data governance policies that dictate how data should be handled, stored, and shared within the organization.

  • Ensure compliance with relevant data protection regulations such as GDPR, CCPA, and others.

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4. Regular Security Assessments: 

  • Conduct regular security assessments, including penetration testing and vulnerability scanning, to identify and mitigate potential threats.

  • Engage third-party security experts for unbiased evaluations.

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5. Model Security:

  • Protect AI models from adversarial attacks by incorporating techniques like adversarial training and using robust architectures.

  • Regularly update and retrain models to ensure they remain effective and secure against new threats.​​

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6. User Education and Awareness:

  • Educate employees and users about security best practices and the importance of data protection.

  • Provide training on recognizing and reporting security incidents.

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By integrating these practices into the development and deployment processes, Givzey significantly enhances the security and integrity of its AI and ML programs, thereby protecting sensitive data and maintaining high security standards.

Accountability, Security, and Oversight

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The same stringent practices in place for all Givzey software also govern any application that uses AI or ML. For more information, please refer to Givzey's:
 

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