As the business case continues to be made to migrate AI and machine learning into business operations, understanding the concepts of the AI learning curve and training your new digital ally is critical. Remember when your daughter tried to get you to help with her “new math” homework and you just stared at her like she was speaking Martian? Imagine doing that with an AI that is supposed to “revolutionize” your business and increase your security footprint. Spoiler alert: it’s not as easy as dragging and dropping a few data sets into a black box and hitting the "make me brilliant" button.
Here is Bob from IT. He is explaining to Chet from upper management what all the AI fuss is about as their organization approaches a potential deployment.
Bob: Chet, AI is like having a personal assistant who never sleeps. It optimizes operations, secures data, and anticipates what we need before we even ask.
Chet: A personal assistant, huh? I don’t need one more thing to manage. How’s this going to help us without becoming another headache?
Bob: It’s not another thing to manage—it’s the manager. AI automates the grind, analyzes trends, and safeguards our business intelligence like a digital Fort Knox.
Chet: Safeguards, sure. But how do we ensure it’s doing what we want, not just what it thinks we want?
Bob: We train it. AI learns from our data and our goals. It’s like teaching a new hire—only this one gets smarter every day and never takes a coffee break.
Chet: And if it goes rogue?
Bob: It won’t. We set the parameters and guide it with our objectives. It’s like molding clay—except this clay knows when to firm up or stay flexible.
This may be a silly scenario, but feeding data to AI requires patience, strategy, and a willingness to deal with some mess. Organizations love to brag about how their AI is “learning” from their data, but they often fail to mention the amount of handholding involved. It's not just about shoving gigabytes of data down the AI's throat and hoping it spits out pearls of wisdom. Nope, there's a whole process of data preparation, labeling, and constant tweaking, much like teaching your dog to roll over after a hundred tries finally.
Organizations leverage their data to "teach" AI intelligence engines through a multi-step process involving data collection, preprocessing, training, and iterative refinement. AI can be trained to fortify critical infrastructure security by addressing physical and cyber threats in several ways.
Physical Security
AI provides proactive strategies to enhance physical security. It can monitor video surveillance feeds throughout the system to detect unusual activities, unauthorized access, or potential threats like intruders, unattended packages, or abnormal movements in restricted areas. Anomaly detection is a key element of advanced AI, as is its propensity to broaden the scope of basic access control systems to include enhanced biometric systems that learn to recognize authorized personnel and flag unauthorized access attempts in real-time. It can also adapt to changing patterns, guaranteeing security protocols evolve with new threats.
AI brings a new perspective to perimeter monitoring. AI-driven drones or robots can patrol perimeters, autonomously detecting and responding to potential threats. Predictive maintenance is another ROI advantage AI brings to the operations side of the argument. By analyzing data from physical security systems (e.g., cameras, locks, sensors), AI can predict when equipment will likely fail, allowing for proactive maintenance to avoid security lapses.
Cybersecurity
Regarding cybersecurity, AI can analyze network traffic to identify suspicious patterns, malware, or phishing attempts, and it can respond by isolating affected systems, alerting security teams, or even neutralizing threats automatically. AI can scan systems for vulnerabilities, assess their severity and prioritize them for patching, helping to maintain up-to-date defenses against known exploits.
Behavioral analytics is an advanced “learned” skill that allows AI to learn the normal behavior of users and systems and then detect deviations that may indicate insider threats, compromised accounts, or other security risks. Of course, the ultimate goal of your AI is to automate the initial stages of incident response, such as containing breaches, collecting forensic data, and initiating recovery processes. This reduces the response time and minimizes damage.
AI’s value in integrating cross-physical and cyber functionality is a growing necessity. From Unified Threat Intelligence, where AI can correlate data from both physical and cyber domains to provide a comprehensive view of potential threats, to Cross-Domain Authentication, which helps AI enforce multi-factor authentication that spans both physical and digital access points and ensures that unauthorized access in one domain cannot be used to compromise the other, critical infrastructure can be better protected against an increasingly complex and evolving threat landscape.
