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The Benefits of Combining Automation and Human Expertise for Cybersecurity

Man and robot sitting at laptops in workplace together. ILLUSTRATION: pch.vector / Freepik

Man and robot sitting at laptops in workplace together. ILLUSTRATION: pch.vector / Freepik

The merger of human expertise and artificial intelligence for the purposes of cybersecurity is already well underway. Market pressures drive the demand for high-level security programs that can address the challenges of today’s cybersecurity landscape. What are the benefits of this merger of man and machine?

Create Safer Networks by Reducing Threats and Attacks

The pace at which cyber-attacks can happen and the speed with which they can breach a network has grown beyond the ability of humans to monitor without the aid of artificial intelligence. By merging these two assets, you reduce the number of successful attacks and can stop threats before they bloom into anything that can pose a severe risk to a network.

In response to an attack, according to a recent interview by cyber security company NTT, “It can also provide a guided response plan with specific suggestions to remediate real or potential threats. These can include basic suggestions to isolate certain systems or more sophisticated directions on how to eliminate threats, going as far as recovering from a potential cyber breach.”

This creates safer networks and builds trust with clients who are concerned for their data and the risk that cybersecurity attacks and other cyber threats pose to their interests.

Reduce Security Costs by Finding and Thwarting Cybersecurity Threats Before Breaches Happen

Security breaches are expensive! According to IBM, the cost of the average security breach in 2022 was $9.4 million. By combining the power of artificial intelligence and machine learning with the ingenuity and creativity of human expertise, you can vastly reduce the cost of cybersecurity attacks. How? Stopping them before they can cause an actual breach.

Artificial Intelligence security programs can predict when a breach may occur by monitoring data in real-time and using past incidents to determine potential threats. Flagging these will bring the attention of a security analyst, which in turn could stop a potential breach, saving the company money and the resources necessary to fix a breach after the fact.

Leverage Artificial Intelligence and Machine Learning to Eliminate Alert Fatigue

Alert fatigue is a direct result of the human need for rest. In the span of a day, a cybersecurity team must deal with numerous potential breaches, perhaps even credible threats of an imminent breach and active security breaches. This level of alertness is challenging for people and their ability to cope effectively is eventually worn down by the constant onslaught of information and stress.

Artificial intelligence and machine learning programs can do the task of investigating whether a potential breach needs a security professional to continue to remove the threat or if it is a false positive.  This leaves the security analyst fresh to investigate and work on real and urgent problems.

Continue to Make Your Artificial Intelligence Better Through Training and High Volumes of Event Detection

The amount of data that an artificial intelligence security program analyzes daily (potentially millions of events), coupled with the training and prompting it receives from the programmers and security team, will lead to a smarter, more robust, and continually more efficient cybersecurity program.

The inherent weaknesses that are present in human analysts (fatigue, limited computing power, etc.) are bolstered by the strengths of the artificial intelligence programs, and the knowledge and experience gaps present in the artificial intelligence systems can be educated and trained upon with the help of the staff. This creates a winning combination for everyone.

Provide Peace of Mind by Combining the Vigilance of Machine Learning Programs and the Ingenuity of Your Cybersecurity Team

Humans are always working on and improving the machine learning algorithms that run within the cybersecurity framework. Through continuous “supervised learning”, these programs grow better at detecting and removing threats. They also can run at varying degrees of autonomy, with the idea that eventually, an artificial intelligence program would be able to do most, if not all, of the cybersecurity work, replacing the human that does that job and allowing for other high-value projects to be completed by those human analysts.


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