Two cutting-edge technologies that are consistently mentioned in market trends and product advancement are artificial intelligence (AI) and machine learning (ML). Often they’re used interchangeably, especially when talking about big data, analytics and other technologies that require processing power, mathematical parsing and data interpretation on massive scales. Unfortunately, these technologies, while related, are not one in the same.
Defining Artificial Intelligence and Machine Learning
This idea of “mechanical men” has been around since ancient Greece. AI can identify its environment and take action based on predefined purposes. AI uses experience such as patterns and anomalies to make intelligent decisions about a task, problem or situation. It relies on past experience and linear events to make a decision. This is why the ability to improvise or work on an instinctual feeling is possible. Keep in mind that the right solution may not be the best solution for the humans involved.
Artificial intelligence, as its name states can “learn” on its own based on human input and results. If A then B, but in some cases C. There is variation in human behavior, thus variation in results based on conditions. Machine learning simply takes the linear logic of AI and can recognize patterns in massive amounts of data at high speeds. Reaction times to events, in this case, a security threat, are faster than a human’s but with human-machine teaming, the volume of data that needs to be analyzed for security threats can be done timely and with more effectiveness.
How Does Artificial Intelligence Complement Cybersecurity?
We’ve reached a time in technology when our machines are not just providing data but collaborating with us to produce better results. In terms of cybersecurity, what are the advantages to utilizing products that include AI and ML in their offerings?
- Machine learning Algorithms identify Malware far quicker than humans. Its pattern recognition helps it to “learn” and become smarter and more effective.
- Machine learning helps improve behavioral analysis of Zero-Day threats and can contain them in a sandbox environment before they’re a commonly known threat or included in a signature file.
- Agents using machine learning Algorithms can hunt and respond to threats on the endpoint without the assistance of an established signature file.
Machine learning also contributes to the ability to parse threat intelligence databases and find Zero-Day threats or phishing examples from around the world that may be trying to intrude upon your system. They’re recognized by analysis and threats based on them are stopped faster than with patches or updates.
Vendors are incorporating these capabilities into their products today. As you look to security solutions, it’s important to consider whether artificial intelligence is working on your behalf as a preventative measure. While this technology does not replace human beings, it does help your resources respond quickly and intelligently when an incident arises.
At Tech Data we have many security products and solutions for both the endpoint and the Managed Service Provider’s Network Operations Center (NOC) that utilize machine learning and the features of artificial intelligence to protect your networks and devices from Zero-Day threats and rapidly replicating global attacks. Connect with us at email@example.com today.