The pandemic sparked a significant surge in computer-related crimes, and in the post-COVID world, businesses are finding new ways to combat cyberattacks using automation and artificial intelligence (AI). An increasing number of cybersecurity patent filings have related to data science, AI and machine learning (ML) in recent years, for instance.

AI is a crucial component of modern cybersecurity measures, as it increases process efficiency, reduces operational costs and resolves issues related to scaling. This is becoming increasingly important as businesses continue adopting new, innovative technologies like cloud computing, mixed reality (MR), blockchain, the Internet of Things (IoT) and other AI solutions to optimize the entire organization — from customer service to accounting and even the C-suite.

Cybersecurity AI solutions bridge gaps in an organization’s security technology and instill a culture of security throughout the organization. It handles repetitive events and flags any outliers to provide continuous, real-time reporting and monitoring of all systems. Best of all, modern AI cybersecurity solutions are easier than ever to use, making it faster to achieve an ROI.

Anatomy of an AI security guard

AI is typically portrayed in science fiction as a sentient being, like Ultron in the Marvel Cinematic Universe, Wall-E and the robots of The Matrix.

While they’re definitely fun to watch, these cinematic adaptations are a lot different than their source material. AI leverages machine learning (including deep learning) and neural networks to process big datasets and complete complex tasks in mere seconds. This unlocks new tools throughout the supply chain, like automated robotics in manufacturing, self-driving vehicles in transport and even fuzzy logic (FL) decision-making skills. When combined with processes like natural language processing (NLP), it’s easy to see how some may believe the AI is alive.

Threat intelligence (TI) and other cybersecurity processes can be more efficient with AI bridging the context gap between humans and operating systems. Modern security is complex and involves a lot of moving parts, which is why the government shifted toward a zero trust architecture moving forward. Remote work and bring-your-own-device (BYOD) policies are more common today, and there are a lot of potential attack vectors and vulnerabilities that only AI can address.

Cybersecurity AI won’t command an actual Ultron army of robots to defend a business, but it does greatly augment a human cybersecurity team with powerful tools that can be used to predict outcomes and automate repetitive tasks. Together, humans and AI are forming the next generation of security teams.

Kinks in AI’s Armor

Although a formidable security force, AI does have potential problems, especially when organizations make mistakes during implementation. A tool is only as good as the person using it, and despite the name, AI still needs human intellect to effectively run it. Common mistakes made in AI implementation include:

• Assuming AI is an actual replacement for a human team and laying off the experts who were keeping the organization safe.
• Not understanding cybersecurity and not identifying the problems so security leaders properly architect the AI.
• Choosing and defining the wrong model to use and not maintaining accurate data within the AI.
• Lack of training for the humans who will ultimately be using the AI.

If security leaders don’t fully comprehend what they’re doing, it’s possible the cybersecurity AI will generate too many false positives to be useful. It could automatically block workers from completing their assigned tasks, effectively shutting down business workflows, and it can keep the cybersecurity staff busy reactively fixing bugs instead of predictively addressing potential security concerns.

5 key components of a successful AI/ML implementation

Before implementing AI into a business, security leaders need to create a framework. AI is a major investment that needs to have a solid blueprint to follow while tracking and measuring success. There are five key components of a successful AI/ML implementation.

1. Planning a comprehensive strategy

Before doing anything, identify the business problem being solved. AI applications are based on specific use cases and need context to be truly effective, so you’ll need to understand the problem well enough to find and validate the right datasets and models with the right capabilities for your needs.

2. Define the model used

Once the problem is understood, it’s important to choose the right model to get the needed results. The AI marketplace is crowded in every sector — including cybersecurity — and security leaders need to start with the right model. Continuously train (and retrain) it to reduce the noise and get the best results from it. Recruit an expert partner if needed.

3. Train and test rigorously

As mentioned, AI models need to be both trained and tested repeatedly with human oversight to be truly effective. After implementing the dataset, security leaders will need to check for accuracy regularly. These datasets dictate how effective the model is, so it’s important to spend as much time as needed to get this stage right before deployment.

4. Deployment transition

Once the AI is ready, it’s time for a phased deployment. Change isn’t easy to handle (for AI or humans), and security leaders need to provide training materials and job aides, along with guidance throughout the organization to be successful. Each phase should have a roadmap and reinforced learning for both the humans and robots.

5. Continuous learning

Once everything is running, it takes work to keep it going. The AI becomes a tool like any other that requires full, ongoing support to address any changes along the way. The only constant in business is change, and security leaders can safeguard their investment through continuous learning and support.

Real-life cybersecurity AI may not have all the fancy special effects seen in movies. However, it can be just as powerful as those robot armies, especially when organizations take the time to understand the problem they’re addressing and build the AI to suit. When done correctly, security leaders can rest easy knowing they equipped their cybersecurity team with the next generation of power tools.