Enterprises face the dual challenge of maintaining innovation while ensuring the security and safety of their AI deployments. As Diane Chang, AI Safety Product Manager at Cohere, aptly puts it,

"Enterprises need to maintain the pace of innovation but also make sure that AI deployments are secure and safe to avoid massive financial and reputational risks."

This delicate balance is further complicated by the ever-growing list of standards and requirements that enterprises must satisfy.

In a recent webinar, Diane joined Prutha Parikh, Head of Security at Cohere, to discuss the critical considerations for enterprises in navigating AI security and safety. The webinar explored Cohere's holistic approach to AI security, emphasizing data privacy, operational security evolution, model security, and customizable safety controls.

By focusing on pragmatic near-term risks and providing enterprises with the tools to set tailored safety guardrails, Cohere empowers organizations to innovate with confidence in the AI era.

To watch the full webinar, visit our on-demand page here.

Top 5 takeaways:

Here are the top insights CTOs and CISOs can take away from the webinar.

Data privacy and compliance

AI systems interact with vast amounts of sensitive data, making data privacy and compliance a top priority. As AI continues to evolve, so too must the strategies for protecting sensitive data and ensuring compliance with an increasingly complex regulatory landscape. Cohere integrates privacy considerations into AI system development from the outset, ensuring data protection and providing enterprises with control over their data.

The scale of data collection and processing in AI systems is unprecedented, leading to heightened privacy risks. AI's ability to infer personal information from seemingly anonymous data sets creates new vulnerabilities. Traditional data privacy principles, such as those outlined in the EU's General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA), must be rigorously applied to AI systems. This includes ensuring data is collected and used only for specified purposes, with clear consent and transparency.

Compliance with these regulations is further complicated by the dynamic nature of AI systems. As models learn and adapt, their data processing behaviors can change, potentially leading to unintended privacy breaches. Organizations must implement robust data governance policies that address the entire AI lifecycle, from data collection and model training to deployment and decommissioning.

Operational security evolution

The evolution of operational security in the context of AI reflects a broader shift from traditional perimeter-based security models to identity-centric approaches. As AI systems become more distributed and integrated into various aspects of business operations, the concept of a secure perimeter becomes increasingly obsolete. Instead, security must focus on protecting identities and controlling access at a granular level.

This shift is driven by the need to secure not just the infrastructure but also the data and models themselves. AI systems often involve complex supply chains, with data and models moving between different stakeholders. Each handoff presents a potential security risk, requiring robust access controls and continuous monitoring.

With AI, identity becomes the new perimeter. Enterprises must evolve their operational security to address the vanishing boundaries of traditional security, focusing on access control, logging, and monitoring within the context of AI-specific security considerations.

Safety modes for customized control

Safety modes are a critical component of Cohere's AI security framework, providing enterprises with the flexibility to customize the behavior of AI models based on their unique requirements. These modes enable customers to set the level of safety guardrail that is suitable for their use cases ensuring consistently safe experiences for the users of their AI systems.

For example, in a customer service chatbot deployment, an enterprise might set “strict” safety mode to prevent the model from generating responses that contain profanity or sensitive information. This ensures that the chatbot remains professional and compliant with company policies, while still providing valuable assistance to customers.

In another scenario, in an assistant that helps content marketers write blog posts, the developer might set “contextual” safety modes to allow end users to research and write about wide-ranging, creative topics, while still preventing abuse of the tool to generate highly harmful content.

By offering controllable safety modes, Cohere empowers enterprises to strike the right balance between innovation and safety. These modes allow organizations to leverage the full potential of AI while maintaining control over the system's behavior, ensuring that it aligns with their specific needs and risk tolerance.

Pragmatic near-term risk focus

Cohere focuses on tackling the most pressing and impactful risks that enterprises face when deploying AI. By prioritizing these near-term challenges, we ensure our AI solutions are practical, relevant, and aligned with the real-world needs of businesses.

For instance, bias in resume summarization is a critical issue that can lead to unfair hiring practices and legal consequences. Cohere meets these customer needs by researching and implementing industry-leading enterprise bias evaluation and mitigation techniques, ensuring that AI models used in recruitment processes are fair and unbiased.

Prioritizing these types of near-term risks, helps enterprises navigate the complexities of AI security and ensures that their AI solutions are not only innovative but also secure, reliable, and aligned with their specific needs.

Model security and supply chain security

The importance of AI model security and supply chain security cannot be overstated. Cohere employs a multi-faceted approach to ensure the robustness and integrity of its AI models. This includes simulating adversarial attacks to identify potential vulnerabilities, evaluating models against established benchmarks to ensure performance and reliability, and conducting vulnerability scans to detect and address security weaknesses.

Adversarial attacks involve intentionally manipulating input data to deceive AI models, testing their resilience against malicious attempts to exploit vulnerabilities. By simulating these attacks, Cohere can proactively identify and mitigate potential security risks, ensuring that models remain secure even in the face of sophisticated threats.

For example, security vulnerabilities in code generation can expose enterprises to significant risks, including data breaches and system compromises. Cohere addresses this by training its models like Command A to incorporate security best practices into its code generation capabilities.

Vulnerability scanning involves systematically examining the model and its associated components for security weaknesses. This includes scanning for known vulnerabilities, misconfigurations, and potential entry points for attackers. By regularly conducting these scans, Cohere can promptly address any security issues and maintain the integrity of its models.

Supply chain security is equally important, as AI models often rely on external data sources, libraries, and dependencies. Cohere implements various security measures to protect the supply chain, including verifying the integrity of external components, ensuring secure data transmission, and maintaining a trusted network of partners and suppliers.

By prioritizing model security and supply chain security, Cohere ensures that its AI solutions are not only innovative but also secure and reliable. This approach helps enterprises mitigate risks, protect sensitive data, and maintain the trust of their customers and stakeholders.

Innovate with confidence

The integration of AI into enterprise operations will only deepen in years to come. Cohere's commitment to secure and safe AI is evident in its holistic approach, which encompasses everything from model development to deployment. Focusing on near-term risks empowers enterprises to integrate AI into their existing products and workflows with confidence.

To learn more about how Cohere is shaping the future of secure AI, explore our Secure AI Frontier Model Framework.

Together, we can navigate the complexities of AI security and unlock the full potential of enterprise AI.

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