Good AI security starts with good security hygiene.
Artificial intelligence is changing how organizations work, but not the fundamentals of cybersecurity. Conversely, the importance of a strong cybersecurity foundation is further enforced by the addition of AI into any work environment.
AI tools can help employees summarize information, analyze data, draft content, and perform research in more efficient ways. While increased productivity matters, those same capabilities can create risk when identities are compromised, data creep is introduced, or AI tools are misused by contributors because of a lack of clear guidance.
The good news is that organizations do not need to solve every AI security challenge at once. The strongest starting point is not advanced tools or complicated governance models. It requires good foundational security hygiene.
Before investing in advanced AI security controls, we encourage our partners to focus on these five fundamentals: identity protection, user access control, approved tools, sensitive data guidance, and basic governance. Let’s take a look at what each of these might mean for your organization.
1. Protecting Identities from AI-related Risks
Compromised accounts remain one of the most prevalent factors of AI-related risk.
When an attacker gains access to a legitimate user account, they can leverage AI to move faster and more efficiently using the victim’s already-granted access and further underscoring the importance of strong identity protection.
In order to protect the data of your team, your clients, and your business, every organization should start with the basics:
- Require multi-factor authentication
- Implement strong authentication methods
- Limit access from unmanaged devices
- Give employees a clear way to report unusual activity
This does not need to feel complicated for end users. The goal is simply to make it harder for attackers to impersonate a trusted user.
2. Review Employee Access Before Expanding AI Use
When implemented properly, AI can only find and correlate data to which an employee already has access, making access reviews one of the most practical AI security steps an organization can take.
Many organizations have some level of oversharing. Often, file access is granted broadly, Teams channels grow over time, old project folders remain long after the project is completed, and employees slowly gain access to more and more data as their roles change.
Before rolling out AI to an environment, review shared locations in SharePoint, Teams, OneDrive and other common places where data lives. Check for broad access, outdated groups, sensitive information not properly locked down, and permissions that no longer match employee roles.
Ensuring that employees only have access to what they need lessens the threat landscape and in turn, what AI can access in the event of a compromise.
3. Use Approved AI Platforms
Every AI tool comes with strengths and weaknesses as well as varied levels of visibility, control, or governance. This further underscores the need for training that teaches employees which tools are approved and why it is important to avoid public and personal AI platforms in a business environment.
Approved enterprise AI platforms can give organizations more control over usage expectations, account access, data handling, and monitoring. While public tools can be useful in some situations, employees need clear boundaries around what they can share and the platforms they can and cannot use.
A simple rule helps: when deciding what to put into an AI platform, employees should consider whether this information would belong on public websites, chats, or relayed in an external email.
The goal is not to discourage AI adoption. The goal is to make safe adoption easier.
4. Think Before Sharing Sensitive Data
People remain a critical control point in AI security.
Employees should understand that AI tools can process and repackage information they provide. This means they should avoid entering passwords, API keys, unnecessary customer data, internal security details, financial information, or confidential business records into tools that are not approved for that use.
This guidance should be easy to remember and follow.
Long policies rarely help employees make better decisions in the moment. Clear examples do.
Organizations should define what is considered sensitive data, the business case for safe AI usage and who to ask if they are unsure.
5. Create Basic AI Governance
AI governance does not have to start with large committees or lengthy policies. At its simplest, governance can mean employees know what is approved, what is not approved, and how to raise concerns.
A basic AI governance program should, at its foundation, answer a few practical questions:
- Which AI tools can employees use?
- What data should never be entered into AI tools?
- Who reviews new AI use cases?
- How should employees report concerns or mistakes?
- How often will access and usage expectations be reviewed?
These expectations help employees use AI confidently and responsibly. In addition, they give IT, security, and leadership a clear foundation for future controls.
AI Security Starts With The Basics
Implementation and usage can feel new, but the first steps are familiar.
Protect identities. Review Access. Use approved platforms. Be careful with sensitive data. Set clear expectations.
These practices will not solve every AI security challenge, but they reduce the risk AI can pose when something goes wrong. Organizations that start with the basics build a stronger foundation for safe AI adoption, better governance, and more confident innovation.
Before investing in advanced AI security controls, ask a simpler question first: are the fundamentals in place strong enough to support how your organization wants to leverage AI?
If you have questions about your organization’s cybersecurity posture, contact us for a consultation.
