Setting the Stage for Safe AI Use in Your Business

AI use is mushrooming in business circles, as nobody wants to be left behind in the race to adopt the latest darling of the tech ecosystem. Organizations in many verticals want to take advantage of automation, turbo-charged forecasting, deep insights, generative content, and many other benefits that AI-powered solutions can bring to every department.

At the same time, however, AI opens a new box of risks. CEOs, compliance officers, and security leaders are concerned about data leaks, security breaches, and regulatory non-compliance due to AI use, all of which can also lead to loss of customer trust.

Unfortunately, their fears are well founded. According to research by Cisco, at least 48% of employees have entered proprietary business information into GenAI tools, and 91% of businesses realize that they need to do more to reassure customers that they only use AI on customer data for ethical and legitimate purposes.

Meanwhile, AI regulations keep coming from all sides. The EU, the US, Canada, China, Argentina, OECD countries, the UN, and the G7 group are just some of the nations and international bodies that have produced AI regulations and/or frameworks in the last couple of years. There are even more in the pipeline.

In this unsteady and risk-strewn landscape, how can enterprises find a safe balance between maximizing the potential of AI and protecting their data and systems from its potential fallout?

Double Down on Cybersecurity Basics

Many of the mainstays of cybersecurity are also fundamental for AI security. That includes powerful access controls, strong encryption, and careful incident response planning. In a similar vein, all your data security policies can generally be applied directly towards the use of AI.

The foundations of any safe enterprise AI program rests in making sure that all your existing cybersecurity policies and protocols are effective and consistently enforced. That also means running ongoing employee cybersecurity training that encompasses coverage of AI-specific concerns, and implementing robust, unified access management solutions.

Zluri, for example, can be used for granular, automated access controls across all your apps and tools, including those that involve AI.

Know What You’re Dealing With

You’re going to struggle to manage your AI if you don’t know what’s in use or understand how it’s being utilized. It’s vital to map and assess all your IT ecosystem, and that includes your AI tools, your legacy IT systems, your data environments and workflows, and the AI solutions your employees started using on the side without consulting security teams.

You need to know which business systems are most critical and which data is the most sensitive, so that you can adjust AI access accordingly.

At the same time, it’s crucial to stay on top of how AI tools are being used on a daily basis, which datasets they can tap into, and what outcomes they are producing. Once you know where your potential security gaps lie, you can start to formulate the policies and procedures to deal with them.

Lean into Regulatory Compliance

There’s a tendency to bemoan the friction caused by AI-related regulations, but they can be a help, not a headache. Existing frameworks, regulations, and industry standards such as NIST’s AI RMF, ISO/IEC 42001, and the EU’s AI Act all offer useful guidelines that can inform your AI policy-making.

With the help of cyber GRC automation platform Cypago, you can take advantage of the guidance offered by regulations. Use the platform to automatically scan your systems for gaps between best-in-class AI frameworks and your AI processes, generate actionable insights into strengthening AI guardrails, and track actions to remediate issues.

You can also set up custom workflow automations in Cypago, based on your own policies, syncing AI security controls with connected systems (code base repositories, HR data, user access controls, project management tools, and so forth) to trigger alerts and remediation steps whenever AI use steps beyond your organization’s acceptable risk thresholds.

Establish an AI A-team

Nobody can manage AI alone, but nobody has to, either.

Turn to your CISO to build an AI consortium that works together to make decisions about risk levels and AI adoption goals. Your AI dream team is key to keeping business AI usage aligned with broader security strategies and objectives.

As you establish your AI oversight committee, think outside the security box. You need to bring together stakeholders from all the relevant departments, including compliance and legal as well as security, if you’re going to develop a comprehensive framework for ethical AI use.

Use Risk Assessment and Prioritization

Last but not least, remember that AI is just another element in your broader risk landscape, and it should be managed accordingly. Your basic risk management principles are still relevant for AI decision-making.

Build a robust risk evaluation framework that’s geared towards AI use, so you can determine what constitutes “acceptable risk” for your organization. Risk assessment tools that are designed for AI, like Google’s SAIF Risk Assessment, help you to measure the risk posed by your AI systems and secure them effectively. Once you’ve run the assessments, you can decide which tools can be given access to sensitive data.

This also forms the basis of incident response plans, enabling you to prioritize resources to the highest-risk situations so that you can address them before they turn into serious incidents.

AI Safety Doesn’t Have to Keep You Awake at Night

Adopting AI tools is already almost non-negotiable for enterprises that want to preserve their competitive edge, but there are ways to keep ahead without compromising business security. When you start with solid cybersecurity, prioritize visibility and compliance, and find the right people and tools to manage the issue, you can take advantage of AI without sacrificing your peace of mind.