Generated AI has not arrived with a bang. It slowly creeps up into the software companies use every day. Whether it’s video conferencing or CRM, vendors are in a hurry to integrate AI Copilots and Assistant into their SAAS applications. Slack can now provide AI summary for chat threads, Zoom can provide a meeting overview, and office suites such as Microsoft 365 include AI assistance in writing and analytics. This trend in AI use means that the majority of companies are awakening to a new reality. The AI features are spread overnight throughout the SaaS stack without centralized control.
A recent survey found that 95% of US companies currently use generative AI significantly in one year. However, this unprecedented use is alleviated by increased anxiety. Business leaders are beginning to worry about where all this invisible AI activity will lead. Data security and privacy are rapidly emerged as the biggest concern, as many fear that confidential information could be leaked or misused if AI usage remains unconfirmed. We’ve already seen some examples of attention. Global banks and tech companies are internally banning or restricting tools like ChatGPT after incidents of sensitive data are inadvertently shared.
Why is SaaS AI Governance important?
With AI woven into everything from messaging apps to customer databases, governance is the only way to leverage profits without incurring new risks.
What does AI governance mean?
Simply put, it basically refers to policies, processes, and controls that ensure that AI is responsible and safe to use within an organization. AI governance prevents these tools from being freed and instead fits the company’s security requirements, compliance obligations and ethical standards.
This is especially important in SaaS contexts where data is constantly flowing to third-party cloud services.
1. Data exposure is the most immediate concern. AI features often require access to a large amount of information. Think of a sales AI that reads customer records, or an AI assistant that compiles calendars, and then think of calling a transcript. Without supervision, unauthorized AI integrations allow sensitive customer data or intellectual property to be used to send it to external models. In one survey, over 27% of organizations said they completely banned the generation AI tools after being scared of privacy. Obviously, no one wants to be the next company in headlines as employees provided chatbot-sensitive data.
2. Non-compliance is another concern. When employees use AI tools without approval, they create blind spots that can lead to violations of laws such as GDPR and HIPAA. For example, uploading client personal information to an AI translation service could violate privacy regulations, but if it was done without knowledge, the company may not know that it will happen until an audit or violation occurs. Regulators around the world are expanding laws on AI use, from the EU’s new AI laws to sector-specific guidance. Companies need governance to prove what AI is doing with their data and to pose fines.
3. Operational reasons are another reason to suppress AI sprawl. AI systems can introduce biases that affect real people and make poor decisions (hatography). Employment algorithms can discriminate incorrectly. Alternatively, as AI changes the model, AI can produce inconsistent results over time. Without guidelines, these issues will not be checked. Business leaders recognize that managing AI risks is not just about avoiding harm, but also about competitive advantage. Those who have begun using AI ethically and transparently can generally build greater trust with their customers and regulatory authorities.
The challenges of managing AI in the SaaS world
Unfortunately, the nature of AI adoption in today’s businesses is becoming more difficult to pin. One of the major challenges is visibility. In many cases, IT and security teams simply don’t know how many AI tools or features are used across their organization. Employees who are passionate about increasing productivity can enable new AI-based features or sign up for a clever AI app in seconds without approval. These shadow AI instances fly under the radar and create pockets of unconfirmed data usage. It’s a classic shadow that has been amplified: you can’t secure something you don’t even recognize as there.
What exacerbates the problem is fragmented ownership of AI tools. Different departments may implement their own AI solutions to solve local issues. Marketing attempts engineering experiments with AI copywriters and AI code assistants, while customer support integrates AI chatbots. Because there is no actual focusing strategy, each of these tools may apply different (or non-existent) security controls. There is no single point of accountability. Important questions begin to fall from the cracks.
1. Who reviewed the security of the AI vendor?
2. Where is the data heading?
3. Has someone set the usage boundary?
The end result is an organization using AI in a variety of ways, with many gaps that attackers can potentially leverage.
Perhaps the most serious problem is the lack of origin of data due to AI interactions. Employees can copy and paste their own text into their AI writing assistant to regain sophisticated results and use it in their client presentations. From the company’s perspective, the sensitive data left the environment without any traces. Traditional security tools may not catch it because the firewall was not compromised and no abnormal downloads occurred. Data was voluntarily given to AI services. This black box effect, where no prompts or output is recorded, makes it extremely difficult for an organization to ensure compliance and investigate incidents.
Despite these hurdles, businesses cannot afford to throw their hands.
The answer is to bring the same rigor to AI applied to other technologies. It’s a delicate balance. Security teams don’t want to be the NO department that bans all useful AI tools. The goal of SaaS AI governance is to enable secure recruitment. That means putting protection into place so employees can take advantage of AI benefits while minimizing downsides.
Five Best Practices for AI Governance in SaaS
Establishing AI governance can be daunting, but breaking it down into several specific steps makes it easier to manage. Below are the best practices that major organizations use to control AI in their SAAS environments:
1. Stock AI use
Start by shining light on the shadows. You can’t control what you don’t know. Be audited for all your AI-related tools, features, and integrations you are using. This includes obvious standalone AI apps and less obvious things like AI features within standard software (for example, the new AI Meeting Notes features in video platforms). Don’t forget about browser extensions and unofficial tools that employees may be using. Many companies are surprised at how long the list looks. Create centralized registrations for these AI assets, and create what they do, which business units they use, and which data they touch. This living inventory serves as the basis for all other governance efforts.
2. Define a clear AI usage policy
Use it exclusively for AI so you are likely to have an acceptable usage policy. Employees need to know what is permitted and what is off limit when it comes to AI tools. For example, you may allow an AI coding assistant to be used in your open source project, but you may prohibit the provision of customer data to external AI services. You must specify guidelines for processing your data (“There is no sensitive personal information in the generated AI app unless it is approved by security”) and review the new AI solution before use. Educate your staff about these rules and the reasons behind them. A little clarification beforehand can prevent many dangerous experiments.
3. Monitor and restrict access
Once the AI tool is played, it monitors its behavior and access. The principle of least privilege applies here: If AI integration requires only read access to the calendar, do not grant permission to modify or delete events. Periodically check the data that each AI tool can reach. Many SaaS platforms provide an administrator console or logging. Use them to see how often AI integration is called and whether it draws an unusually large amount of data. If something is off or looking at the outside policy, be prepared to intervene. It’s also wise to set up alerts for specific triggers, such as employees trying to connect their corporate apps to new external AI services.
4. Continuous risk assessment
AI governance is not a set, it forgets tasks. AI changes too quickly. Establish a process to reassess risk on a regular schedule, monthly or quarterly basis. This includes rerunning the environment for newly introduced AI tools, reviewing updates or new features released by SaaS vendors, and staying up to date with AI vulnerabilities. Adjust policies as needed (for example, if your research reveals a new vulnerability, such as a rapid injection attack, update controls to deal with it). Some organizations have formed AI Governance Committees with stakeholders from security, IT, legal and compliance to continue to consider AI use cases and approvals.
5. Collaboration that transcends the scope of work
Finally, governance is not merely an IT or security responsibility. Make AI a team sport. We will bring legal and compliance officers to interpret new regulations and ensure that your policies meet them. Include business unit leaders to ensure governance measures meet business needs (and they act as champions of responsible AI use in teams). Involving data privacy experts, AI assesses how data is being used. When everyone understands shared goals – using AI in an innovative and safe way – it creates a culture that allows following the governance process to enable success and not hinder it.
To translate theory into practice, use this checklist to track progress.
By taking these basic steps, organizations use AI to improve productivity while protecting security, privacy and compliance.
How RECO simplifies AI governance
Establishing an AI governance framework is important, but the manual effort required to track, monitor and manage AI across hundreds of SAAS applications can quickly overwhelm your security team. This is where professional platforms like Reco’s dynamic SaaS security solutions can make the difference between theoretical policy and practical protection.
Get a RECO demo to assess AI-related risks in saaS apps.
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