Implementing AI within your security strategy: 7 best practices

There’s a ton of media hype about the swift integration of AI across different business functions. It has also been reported that 98% of technology executives have paused their AI programs to establish guidelines and policies around its implementation. Depending on when and where you read about it, opinions on the speed of AI adoption vary. Nevertheless, AI is more than just hype.

In the past 18 months, we have witnessed a tectonic shift that looks set to be even more impactful than the cloud. Why do we and many other industry experts believe this to be true? Manual data analysis has always been challenging, but the explosion of data from digital transformation, IoT devices, and other sources has made it impossible. According to IDC, 90% of data generated by organizations in 2022 was unstructured, meaning most of this data wasn’t accessible without brittle, custom solutions.

What makes AI capabilities a game-changer for any data-driven organization is that all of this previously invisible data can now be made visible, whether it’s documents, code, imagery or video.

Employing your inner skeptic 

As with any security or IT solution, it's important to be cautious when considering what AI-driven technologies or platforms with AI capabilities to add to your tech stack. Many may seem impressive during a demo, but disappoint when deployed in a real-world setting. Not all AI capabilities can guarantee security and privacy, so they could introduce new risks to your environment.

Even with a trustworthy solution in place, the adoption of AI technologies isn't a simple binary decision. There's a spectrum of possibilities, particularly when it comes to integrating AI into your workflows.

Ultimately, the goal should be to have AI and machine learning models handle the essential repetitive work, freeing up people for more complex edge cases and work that requires creativity and critical thinking. 

7 best practices for implementing AI into your security strategy 

1. Be thorough when evaluating AI tools 

Ensure your solution of choice is deployable - solves real problems, is flexible, runs where you need it to at scale and is cost-effective. We’ve provided a handy checklist of questions to ask when evaluating AI tools to help you get started. 

2. Understand your model and pricing options 

Ensure your solution gives you access to a choice of secure and private models. That way, you can match the sophistication of the model to the sophistication of the task, and ensure your solution remains cost-effective.

3. Start by introducing AI to low-risk parts of your workflows 

Introduce AI in areas where the risk of failure is minimal. This helps in getting accustomed to the technology and understanding its impacts without jeopardizing critical business operations. 

4. Make incremental investments 

Start by using an affordable LLM for a straightforward task like analyzing phishing emails, and gradually increase your usage and investment as you build trust. 

5. Keep humans in the loop to ensure your workflows are running as expected 

Human oversight is always crucial. Take time to understand why AI makes certain decisions to detect any errors and biases.

Keeping humans in the loop will help ensure AI systems are doing what they are supposed to and will notify you and facilitate the need for manual intervention if something breaks midway through a workflow or a service you’re relying on goes down.

6. Monitor what AI is driving to adapt and improve your workflows as needed 

Continuous monitoring allows you to assess the performance of AI and make necessary adjustments. This adaptability can drive more effective and efficient operations.

7. As you build trust, increase your use of AI in your workflows  

Gradually increase the scope of AI in your processes as your proof of its capabilities and reliability grows.

Balancing innovation and security 

Technologies that are advancing rapidly, like AI and security, require a delicate balance. Tines is secure by design. None of our users' data is vulnerable due to the extensive guardrails we have in place.

Tines' intuitive workflow platform enables teams to automate their most powerful workflows with human input and oversight whenever needed. AI features allow users to build workflows faster, run better workflows, and monitor anything that isn’t behaving as expected. 

We’ve previously discussed how burnout from excessive workloads and insufficient staffing contributes to 66% of employees leaving their jobs. Workers are increasingly seeking employment opportunities in companies that empower them with advanced tools and technologies. Businesses that fail to embrace AI risk being left behind as talent migrates to more technologically progressive workplaces.

So, how can businesses restart their AI initiatives? The key is to set objectives aimed at building trust in AI systems. Understand the required guardrails to build this trust, such as better-trained personnel, improved visibility, enhanced transparency in the decision-making process, and thorough oversight and auditing mechanisms. Knowing what these guardrails are allows you to systematically implement AI, ensuring your organization doesn't fall behind.