How to build an oversight plan for AI and cloud governance

With nearly 4 in 5 companies having adopted AI technologyOpens a new window and the overwhelming majority of businesses embracing cloud computing,Opens a new window most organizations are smarter and more well-connected than ever. At the same time, all these new tools and technologies also introduce new potential hiccups for IT pros. This makes it more critical than ever for companies to establish robust oversight practices for working with these technologies. Without such policies in place, you risk overlooking major compliance issues, encountering security vulnerabilities, and dealing with prospective points of network failure.

It’s also important to note that establishing a successful plan for AI and cloud oversight here isn’t just about managing the ways your team uses these solutions, either. It’s also about strategically plotting out how you can use AI and cloud services to create value for your business in a secure, ethical and legally-compliant manner. Putting a strong oversight framework in place not only helps you ensure that your AI and cloud operations are aligned with business objectives, it also helps you minimize the associated risks that come with them. Let’s take a closer look at what it takes to craft a winning strategy for managing your cloud and AI operations.

Define clear roles and responsibilities

The first piece of an effective oversight plan is to make sure that everyone in your organization knows the role that they’ll play and responsibilities that they’ll have in terms of decision-making and accountability.

• Executive and C-suite management: Senior leadership teams should take ownership of the overarching AI and cloud governance strategy. In practical terms, that means they should be setting goals, securing budget allocations, and ensuring big-picture alignment with business objectives. Top execs should also be responsible for ensuring compliance with privacy, security, and regulatory standards.
• Governance committee for AI and cloud ops: Oversight groups typically consist of cross-departmental stakeholders, including folks from the IT, legal, data science, security, compliance, and risk management teams. Members’ job is basically to provide oversight, create policies, and ensure that AI models and cloud deployments are both meeting corporate goals and meeting legal and regulatory standards.
• Project managers for AI and cloud: Act as producers who oversee the day-to-day implementation of AI and cloud technologies. The job in this role is to make certain that projects stay on track, align with governance policies, and are delivered on time and within budget.
• Data governance pros: These teams manage data quality, integrity, and security, ensuring that any information that is used by your AI models and cloud services is accurate, well categorized, and being properly utilized.

Having a clear governance structure in place up-front helps to ensure that decision making goes smoothly. It also helps to make sure that all stakeholders understand their responsibilities and how to exercise day-to-day oversight in practice.

Promote AI and cloud security

Security is paramount when you’re managing AI systems, especially those using information that resides in the cloud. Since cloud environments tend to be accessed remotely, and from multiple locations or devices, they’re a prime target for cybercriminals. That makes implementing a comprehensive cloud security plan across your organization critical to safeguarding sensitive data, networks, applications, and AI tools across the board.

As you design an oversight plan, be sure to include:

• Data encryption: You’ll want to encrypt sensitive data both when it is in transit and when it is at rest. That also means having to double-check and ensure that any cloud service providers’ tools that you are using are properly configured and monitored as well. Providers like Amazon Web Services, Google Cloud and IBM Cloud can help.
• Identity and access management: You and your teams can use these tools to control who has access to cloud resources and AI systems. You’ll want to put multi-factor authentication (MFA), role-based access control (RBAC), and least-privilege access policies in place as well, which can help you reduce the risk of unauthorized access. Companies such as Okta, Ping Identity and Auth0 offer solutions that can assist.
• Incident monitoring and response: It’s vital to continuously monitor cloud operating environments and applications for signs of suspicious behavior, and to quickly detect and respond to any anomalies or breaches. Not only should you use cloud-native tools to monitor activity in real-time, you’ll also want to have a formal incident response plan in place to quickly identify and mitigate any security breaches that do arise. Online security firms such as Sophos, Crowdstrike and Palo Alto Networks offer readymade incident tracking and response capabilities and platforms.
• Safeguards against third-party risks: Any AI, and cloud providers that you use may rely on their own third-party vendors or software, creating additional sources of risk. Therefore it’s vital for you to perform due diligence to assess the cybersecurity policies of any third-party vendors you use to make sure that they (and their tools and collaborators) adhere to your organization’s security standards.
• Routine audits: Proactively schedule periodic system audits to verify that your staffers are following the policies you’ve put in place. In addition to maintaining oversight, auditing can also help determine where you need to adjust your policies.

Promoting a winning cybersecurity security isn’t a one-time task. It requires you and your teams to continuously monitor and adapt to new threats. Instituting a robust governance plan in your business will help strengthen your security posture, and help keep your organization’s AI and cloud environments more secure.

Maintain regulatory compliance

Any sound AI and cloud governance framework should be designed to maintain compliance with industry rules and regulations. Given that governments worldwide are only continuing to introduce new privacy and data protection laws, protocols and practices that you employ shouldn’t remain static either. It’s important for your organization to stay on top of any changes in the market or regulatory space and ensure that your AI and cloud operations are fully compliant.

Sample regulations that you may have to plan IT systems to handle are:

• GDPR (General Data Protection Regulation): Imposes detailed rules and regulations on data privacy, consent, and processing for organizations seeking to operate in the territories of the European Union or handle the data of EU citizens.
• CCPA (California Consumer Privacy Act): California is one of the 20 states that currently have laws regulating the collection and usage of personal data for state residents.
• HIPAA (Health Insurance Portability and Accountability Act): It imposes rules on the protection of sensitive health data that medical providers, systems and applications might seek to exchange.
• Sarbanes-Oxley Act: Putting effective AI and cloud governance in place for companies subject to financial regulations means that firms must ensure that related data and records are accurate, auditable, and stored securely.
• Industry-specific regulations: Many industries have their own field-specific regulations with requirements for how cloud and AI tools are allowed to handle and process sensitive data.

Given these regulatory needs, your AI and cloud governance oversight plan should include processes for staying up to date and regularly reviewing relevant regulations, assessing any gaps in compliance, and updating systems in a timely fashion.

Strong data governance 

Data is the lifeblood of any modern business, and the fuel for both AI and cloud-based systems of all kinds. Without proper data governance policies to ensure that information is accurate and uncompromised, AI models can become flawed or biased, and cloud environments can become vulnerable to security and compliance risks. Making a point to establish and utilize strong data governance policies is a major component of designing and implementing a robust oversight plan.

You’ll want to design your governance protocols to address:

• Data quality: Make sure that any information used for AI modeling or in cloud applications is accurate, complete, and consistent. Data of poor or questionable quality can lead to incorrect insights and decisions.
• Access and management controls: Limit access to sensitive data through role-based and least-privilege access models to reduce the risk of exposing sensitive information.
• Classification and cataloguing: Implement a data classification system that allows you categorize information based on its level of sensitivity. Doing so enables for more effective data management, access control, and encryption practices.
• Lineage and provenance: Track and document the origin of any data and understand how it flows through your company’s AI- and cloud systems. This can help ensure greater transparency, auditability, and accountability in your operations.
• Deletion and retention policies: Maintain clear data retention policies and establish protocols for deleting or anonymizing it when the information is no longer needed.

Performing these data governance functions isn’t just necessary for maintaining productive operations and accurate strategic planning. It’s also critical for ensuring compliance with privacy regulations and safeguarding sensitive business information from falling into the wrong hands.

Continuous monitoring and reporting

It’s essential for you and your teams to establish processes for continuous monitoring and reporting. Doing so helps to ensure that any networks, systems and applications that you use are functioning as intended. It also helps identify any emerging risks or gaps in your compliance protocols.

As a general rule, monitoring and reporting practices should involve:

• Routine audits: Proactively run periodic audits of AI and cloud systems to verify that governance controls are being properly followed and that security measures are effective. Revise and update as needed.
• Tangible KPIs and metrics: Track measurable metrics and tangible key performance indicators (KPIs) related to AI model performance, cloud security, data privacy, and regulatory compliance, among other areas.
• Plans for incident response: Put a formal process in place for reporting and responding to potential incidents related to issues such as AI errors, cloud security breaches, or data privacy violations.
• Reporting systems for stakeholders: Institute systems that allow you to regularly report to key stakeholders on the status of your AI and cloud governance. Doing so helps to foster greater accountability in the organization, and also helps leadership teams remain engaged in risk management and compliance.

Instituting effective monitoring and reporting practices helps to make sure that your AI and cloud environments remain secure, compliant, and aligned with your organization’s big-picture governance objectives.

Putting your AI and cloud governance plan in place

Building and managing a comprehensive AI and cloud governance oversight plan is no small task. At the same, it’s an essential step for organizations looking to embrace such transformative technologies while also minimizing exposure to an ever-growing assortment of IT and cybersecurity risks.

Taking the time out up-front to invest in and implement a strong oversight plan before adopting AI or expanding your cloud infrastructure ensures that your organization can operate and innovate with confidence. Once established, IT pros and leadership teams can rest easier knowing that they’ve put the platforms, people and processes in place to more effectively protect data, and stay compliant with future legal and regulatory shifts.