Since those heady days when generative AI suddenly smashed its way into the world, you can’t go five minutes without someone mentioning the term AI.

This is for numerous reasons, but primarily due to the way it has upended many conventional aspects of how we live and work, and has massively changed the prism through which most businesses view the world.

With evolving threats, regulatory pressures, and growing concerns over data integrity and ethical AI use, aligning your SaaS offering with established AI security standards is not really an option anymore, but rather more of a necessity if you want to compete with other providers and remain on the right side of the regulators.

But putting this into action isn’t always as straightforward as it first appears… which is why we’re here to offer a few suggestions on how you can achieve this task.

Assess Current SaaS Platforms’ Security Measures

This might be an obvious first step, but it’s one that any sane manager needs to complete before implementing anything new into their operations. Whether you’re choosing one of the leading Rust auditing services for 2025, implementing Rust-based smart contracts, or seeking to connect an existing platform to an AI agent to utilize all the advantages that LLMs bring, spending time considering what you need vis-à-vis security measures will help you avoid many of the pitfalls that come from hasty implementation of anything.

To go about this task, you will need to assemble all relevant stakeholders and employees and discuss where your project currently stands, what you need to add in terms of security, how you can achieve it, and then develop a sensible timeframe that allows you to put your plan into action.

Identify AI Security Standards Relevant To Your Product

This step is more complex than the previous one and involves:

• Assessing your AI use cases… For example, are you utilizing AI for personalization, analytics, fraud detection, automation, or other purposes? Knowing that different applications carry different risks and what they are will help you to ultimately come to a decision.
• Creating a map to applicable standards or frameworks… When it comes to AI security standards, there are plenty to choose from. But if you’ve assessed your use case as per the previous point, you should narrow down the options that work for your platform and customers.
• Evaluating legal and regulatory compliance needs… In all honesty, this will likely be your biggest headache, particularly if you offer your platform in different regions with differing frameworks. Your best bet is to bring your legal counsel into the fray to offer you advice.
• Prioritizing based on risk and reach… Not all standards are the same, and by placing those that align with your product’s architecture, threat exposure, and consumer expectations at the top of your list, you will achieve the most comprehensive outcome.

Conduct A Security Gap Analysis

To most completely align your platform with security standards (especially those of AI use, which can be highly complex), you will have to perform what is known as a security gap analysis.

This is precisely what it sounds like, and is the process of identifying weaknesses between your current security setup and the standards you aim to meet. In the context of SaaS or AI-enabled platforms, this means evaluating how your system handles data encryption, access control, threat detection, AI model integrity, and more.

How you go about this task will vary based on the size of your team and other factors.

Once completed, you will have a much better understanding of where any security gaps lie,

It provides you with the best information regarding how to plug them.

In some ways, this job is as challenging as the steps needed to take to remedy them, because you may discover more that needs repairing than you think. However, if you take your time and make sure to set up an all-encompassing audit, you are more likely to spot the sorts of things that will keep your platform head and shoulders above the competition.

Train Staff On Updated Protocols

Regardless of what you’re implementing into your platform, you always need to develop a new staff training manual so that your team understands the latest protocols and how to navigate them when creating the application.

You can update existing training programs or choose to generate a new set entirely based on how deeply they’re integrated and how they involve your team.

You will also want to document all new procedures for future reference and to ensure smooth onboarding of any new staff you might hire.

This step can dramatically enhance productivity by ensuring that all protocols are located in one repository that is easily accessible to anyone wishing to access them. For best results, use a cloud-based Learning Management System (LMS) that can be updated on the fly, has permission-based access to ensure only those with the correct permissions are allowed to access it, and offers the ability to access a history so you can go back and edit any mistakes.

You might have trouble with this step (which is entirely possible, especially for smaller organizations).

But you can often outsource this task to businesses that specialize in developing and setting up access to these kinds of highly specialized staff training programs. As long as you select one that understands the industry you’re in, you will find they are able to craft something perfectly tailored towards your firm and employees.

Regularly Monitor Compliance With Standards

When it comes to technology, as advanced as AI, you should assume that breakneck is the speed at which things will advance.

As such, you should regularly monitor whatever you’ve implemented against current standards to ensure it still matches.

To maintain alignment, it pays to establish a structured process for continuous evaluation, including:

• Automated monitoring tools to detect anomalies — By automating this process, you will save your business a huge amount of resources and ensure a much higher level of accuracy than attempting to go via a manual route. Popular tools include vulnerability scanners like Nessus and Qualys, as well as network mapping and scanning tools like Nmap, all of which address different aspects of the same puzzle.
• Regular audits of AI models for bias or drift — It’s well-known that AI models of all stripes have a propensity to drift or “hallucinate,” but they are also prone to biases from the simple fact that they have been developed on content that humans have created over time. Auditing your systems for signs of this bias can also be performed using automation; however, for added security, it may be worthwhile to include a human element to double-check.
• Updates to encryption and access control mechanisms to counter new cyber threats — As soon as any new threats emerge, you ought to put your auditing protocols into action. This will ensure that you are able to patch any vulnerabilities and stay one step ahead of the game. It might be costly, but it’s far more affordable than putting out fires as they hit your systems.

Adopting AI and implementing it into an existing product is NOT something to be taken lightly, particularly when it comes to security standards.

Incorporating any sort of new technology is often a challenge (with AI still considered to be new technology), but by following these steps, you will make the process more manageable.