How to create safe business models for AI

Over the last five years, enterprise AI use has increased, and CEOs say investors, creditors, and lenders are pressuring them to deploy generative AI. The knowledge that we’ve reached a new AI maturity level opens up new options, results, and cost advantages for society.

As AI mystery erodes confidence, many companies have been hesitant to go “all in” on it. Security is often considered an unknown. AI model security: how? How can you safeguard this transformational technology against data theft, manipulation, leakage, evasion, poisoning, extraction, and inference cyberattacks?

The global race to lead in AI among governments, markets, and businesses has increased pressure to address this issue. AI models’ increased “attack surface” new to everyone and changing data make safeguarding them difficult. To control an AI model or its results for harmful purposes, attackers may try to hack multiple entrypoints, many of which we’re currently finding.

This problem has a solution. We’re seeing the greatest crowdsourced AI security movement ever. The Biden-Harris Administration, DHS CISA, and EU AI Act have rallied the research, developer, and security communities to advance AI security, privacy, and compliance.

Enterprise AI security

Understanding that AI security goes beyond AI itself is crucial. In other words, AI security goes beyond models and data. Another defense for AI is the corporate application stack that embeds it. Because an organization’s infrastructure might be a security vector that gives attackers access to its AI models, we must secure the environment.

To understand how to protect AI data, models, apps, and the complete process we must understand how it works and how it is distributed across multiple contexts.

Enterprise application stack hygiene

The initial protection against AI model risks is an organization’s infrastructure. Integrating security and privacy controls into AI IT architecture is crucial. We know how to set optimum security, privacy, and compliance requirements in today’s complex and dispersed situations, giving the industry an edge. We must also acknowledge this everyday duty as a safe AI enabler.

For instance, secure user, model, and data access is crucial. Using current controls to secure AI model routes is essential. Similar to how AI adds visibility to corporate apps, threat detection and response must be extended to AI applications.

Preventing exploitation requires table stake security requirements including secure transmission throughout the supply chain, strict access controls and infrastructure safeguards, and improved virtual machine and container cleanliness and controls. We should apply our enterprise security strategy to the organization’s AI profile’s protocols, rules, hygiene, and standards.

Training and use data

Even though AI lifecycle management standards are still emerging, enterprises may use current guardrails to protect the AI journey. Transparency and explainability avoid bias, delusion, and poisoning, therefore AI adopters must build standards to monitor workflows, training data, and outputs for model correctness and performance. Data origin and preparation should also be disclosed for trust and transparency. This context and clarity may assist identify data irregularities early on.

Privacy and security must be enforced throughout AI development and deployment, including training and testing data. AI models learn from their underlying data, therefore it’s crucial to account for that dynamic, address data accuracy issues, and include test and validation stages throughout the data lifetime. SPI, PII, and controlled data leakage via prompts and APIs must be detected and prevented.

Lifecycle governance of AI

Building, implementing, and regulating AI programs must be linked for security. AI must be governed, transparent, and ethical to meet regulatory requirements. As enterprises consider AI adoption, they must assess open-source providers’ AI model and training dataset policies and platform maturity. it consumption and retention should be considered, including how, where, and when it will be utilized and limiting data storage lifespans to decrease privacy and security threats. Additionally, procurement teams should verify consistency with the enterprise’s privacy, security, and compliance rules and standards, which should form the basis of any AI policy. 

Securing the AI lifecycle requires adding ML to DevSecOps procedures while creating integrations and deploying AI models and apps. AI model and training data handling is crucial to system integrity, as is pre-deployment training and version management. Monitoring prompts and AI model access is crucial.

This is not a complete introduction to AI security, but it aims to dispel myths. Security tools, procedures, and strategies for AI implementation are currently available.

AI security best practices

As AI use grows and innovations improve, security recommendations will mature, as with any technology that’s been part of an organisation. The following IBM recommended practices may help enterprises secure AI implementation across various environments:

• Evaluate vendor policies and procedures to use trustworthy AI.
• Allow secure user, model, and data access.
• Protect AI models, data, and infrastructure against adversaries.
• Protect data throughout training, testing, and operations.
• Incorporate threat modeling and security coding into AI development.
• Detect and respond to AI application and infrastructure threats.
• Use IBM AI framework to evaluate AI maturity.

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