Establishing Secure AI+ Business Models: Best Practices and Considerations

1:27 pm
September 18, 2023

Summary: The adoption of artificial intelligence (AI) by enterprises has doubled in the past five years, with significant pressure from various stakeholders to accelerate deployment. However, concerns about the security of AI models have limited widespread adoption. Securing AI is a complex task, as it requires protecting not only the models and data but also the broader enterprise application stack. Fortunately, efforts are underway to address these challenges, with initiatives from the Biden-Harris Administration, DHS CISA, and the European Union’s AI Act. This article explores best practices for securing AI and emphasizes the need for a holistic approach to AI security.

Securing AI for the Enterprise

Securing AI goes beyond protecting the models and data. It also involves securing the enterprise application stack where AI is embedded. This includes implementing security controls for user access, threat detection, and response, and following standard security protocols across the organization’s infrastructure. By extending security practices to AI, organizations can enhance the protection of their AI models and ensure a more secure environment.

The Role of an Enterprise Application Stack’s Hygiene

The organization’s infrastructure serves as the first line of defense against threats to AI models. Implementing proper security and privacy controls within the broader IT infrastructure is crucial. This includes establishing secure access to users, models, and data, as well as ensuring threat detection and response capabilities cover AI applications. By adhering to standard security protocols, such as employing secure transmission methods, access controls, and infrastructure protections, organizations can prevent exploitation and strengthen the security of their AI systems.

Usage and Underlying Training Data

Securing AI also involves considering the entire AI lifecycle, including the training and testing data phases. Organizations can leverage existing guardrails to protect the AI journey. Transparency and explainability are essential to prevent bias and malicious attacks, so protocols should be established to audit workflows, training data, and outputs. Additionally, documenting the data origin and preparation process can help detect anomalies and maintain data accuracy. Data loss prevention techniques are crucial to detect and prevent data leakage and protect sensitive information.

Governance Across the AI Lifecycle

Securing AI requires an integrated approach to building, deploying, and governing AI projects. Organizations should consider the governance, transparency, and ethics of AI models and datasets. This includes evaluating open-source vendors’ policies and practices, establishing data usage and retention policies, and aligning AI policies with existing privacy, security, and compliance guidelines. Additionally, integrating AI into current DevSecOps processes and continually training AI models can enhance system integrity and protect against potential threats.

Best Practices to Secure AI

As AI adoption continues to scale, security guidance will mature, similar to other technologies. Here are some best practices from IBM to help organizations prepare for secure AI deployment:

  1. Leverage trusted AI by evaluating vendor policies and practices.
  2. Enable secure access to users, models, and data.
  3. Safeguard AI models, data, and infrastructure from attacks.
  4. Implement data privacy protection in all phases of AI.
  5. Incorporate threat modeling and secure coding practices into the AI development lifecycle.
  6. Perform threat detection and response for AI applications and infrastructure.
  7. Evaluate AI maturity using established frameworks.

By following these best practices and adopting a comprehensive approach to AI security, organizations can establish secure AI+ business models that mitigate risks and build trust in the technology.

Frequently Asked Questions (FAQ)

1. Why is securing AI important for enterprises?

Securing AI is important for enterprises because it protects the AI models, data, and infrastructure from various cyberattacks, such as data theft, manipulation, and leakage. By ensuring the security of AI, enterprises can maintain the integrity and accuracy of their AI systems, build trust with stakeholders, and prevent potential financial and reputational damage.

2. How can organizations secure their AI models?

Organizations can secure their AI models by implementing a holistic approach to AI security. This includes securing the enterprise application stack, implementing security controls for user access, and incorporating threat detection and response capabilities. Additionally, organizations should follow best practices such as evaluating vendor policies, protecting data privacy, and integrating AI into existing DevSecOps processes.

3. What are the risks associated with AI?

AI poses several risks, including data breaches, biased outcomes, and adversarial attacks. Without proper security measures, AI models can be manipulated or compromised, leading to inaccurate results and potential harm. There is also the risk of privacy violations if sensitive information is mishandled. Therefore, it is crucial for organizations to prioritize AI security to mitigate these risks.

4. Are there regulations or initiatives to promote AI security?

Yes, there are initiatives and regulations in place to promote AI security. For example, the Biden-Harris Administration, DHS CISA, and the European Union have launched efforts to drive security, privacy, and compliance for AI. These initiatives involve mobilizing the research, developer, and security communities to collectively work towards enhancing AI security.

5. How can organizations ensure transparency and explainability in AI?

To ensure transparency and explainability in AI, organizations should establish protocols to audit workflows, training data, and model outputs. By documenting the data origin and preparation process, organizations can detect anomalies and maintain data accuracy. Additionally, organizations should adopt practices that allow stakeholders to understand how AI models work and address any potential biases or risks.


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