Wit Jakuczun,

AI for Cybersecurity: Breaking Down the Wall of Worry

Discover how AI for cybersecurity can lower the "wall of worry" by proactively detecting threats and enhancing protection. Boost your defenses now with advanced AI technologies.

Discover how AI for cybersecurity can lower the “wall of worry” by proactively detecting threats and enhancing protection. Boost your defenses now with advanced AI technologies.

The financial sector is not the only one facing a “wall of worry.” Many organizations are dangerously close to hitting cybersecurity’s wall. On Wall Street, the wall starts when outside factors that should impact the market negatively are offset by active buying of stock which allows the financial markets to continue to rise. Investors worry about a possible correction that would cause markets to plunge.

For IT departments, the wall of worry happens when the cybersecurity workload exceeds the department’s ability to manage the infrastructure, creating an environment where potential threats are overlooked. The simplest way to lower the wall is to hire more staff, but that is not an easy task in today’s job market.

Anyone looking to fill a cybersecurity position knows there is a shortage of qualified cybersecurity candidates. It is estimated that x positions are unfilled because of a lack of security specialists. This gap will only increase as cybercriminals continue to escalate their attacks. Today, a cyber event happens every 11 seconds, up from 39 seconds in 2007.

If organizations cannot add to the workforce, they need to look at reducing the workload. But what piece of the cybersecurity puzzle is pushed aside? Do IT departments stop looking at network event logs? Do they assume that everyone accessing the system can be trusted? How can they proactively protect the company’s digital assets when there’s no time to analyze the data?

Artificial Intelligence

For cybersecurity teams to protect a company’s infrastructure, they need visibility throughout the enterprise. They need to know that the IoT device at the edge is not vulnerable; in the same way, they need to know that employees are who they say they are. Teams want data to help them proactively address cybersecurity concerns instead of operating from a defensive position.

Machine learning (ML) can ease the pressure on cybersecurity departments. With its ability to learn and improve, artificial intelligence (AI) can analyze hundreds of alerts and notifications to address vulnerabilities proactively. Here are five ways AI can help.

Threat Detection

The adage, “A good defense is the best offense,” is the underlying philosophy of any cybersecurity program. Countering a threat before it worms its way into a network can save organizations millions. Not only are cyberattacks costly, averaging $3.86 million, but they are also time-consuming. On average, it takes close to nine months to contain an attack.

AI can ingest the hundreds of thousands of alerts and notifications a cybersecurity team receives to identify potential threats or system anomalies. As the solution works, it learns a system’s behavior making it easier to identify potential threats. AI’s capabilities enable it to evolve to detect changes in threat vectors that humans would not be able to see.

Technology can analyze volumes of data looking for patterns that can predict future actions. Whether it’s a change in the types of attempts or unusual behavior at an endpoint, AI can spot the pattern long before a human-based system can. In this way, AI improves protection and frees IT staff to work on projects best suited for humans.

Threat Prevention

AI for Cybersecurity: Breaking Down the Wall of Worry

Hackers use phishing techniques to gain access to employee credentials. They may send emails, even texts, that appear to be from legitimate sources. When recipients select the link, they are routed to a phishing source that collects information or installs malware. Phishing is so prevalent that one in every 100 emails is probably an attempted attack.

Some websites are known to be fraudulent and many antivirus solutions attempt to check the legitimacy of incoming emails. With AI, systems can check network traffic for unusual activity and compare URLs against a list of known phishing sources to provide added protection. With over 10,000 active phishing sources, that task is not feasible for a human to perform.

Vulnerability Identification

Every system has vulnerabilities or weaknesses that can be exploited for financial gain. Some are in applications. Others may be created through configuration errors. Whatever the reason, many vulnerabilities go undetected until a hacker decides to use them. The US government maintains a database of known vulnerabilities to assist the public and private sector in developing their cybersecurity programs.

According to the NIST, over 1500 vulnerabilities were identified in one month. Not all vulnerabilities pose cybersecurity risks, but it’s not possible to determine that by looking at a number. What is impossible is having humans check the ever-growing database against a corporate enterprise to determine the potential risk.

AI and ML systems are perfect for vulnerability assessments. They can look for known vulnerabilities and alert IT departments if a weakness is found. They can also identify potential system targets by combining data from forums, trends, and other online sources.

Access and Authentication

According to Verizon’s latest data breach report, over 60% of all attacks use compromised credentials. That means those usernames and passwords may be the weakest link in any cybersecurity plan. Companies should look at multi-factor authentication and least-privilege models to strengthen their access and authentication processes.

AI can help identify possible intrusion by analyzing end-user activity. With a least-privilege model, user access is restricted to those components of the system that they use every day. No one is given complete access. By limiting access, it makes it more difficult for hackers to move within the system, and it creates a user-specific pattern that can be monitored.

ML solutions can learn the usual behaviors for each end-user. If a user logs on at a different time or location, AI can alert someone or lockout the user until the threat can be assessed. For enterprises with thousands of employees, monitoring user-specific activity is not feasible.

Network Security

AI can monitor networks and identify potential sources of possible cyberattacks. It can also help enforce security policies and procedures. No one really wants to talk about the administrative side of security, but the governance of digital assets is a crucial IT responsibility. If standard procedures are in place, AI can note when a user deviates from the standard.

Noting deviations helps identify possible bad actors and can help with locating a vulnerability that was created by a configuration change. Over time, AI may be able to suggest changes to improve network functioning. Placing as many administrative tasks as possible under AI technology frees IT staff for more challenging efforts.

Lower the Wall of Worry With AI for Cybersecurity

AI for Cybersecurity: Breaking Down the Wall of Worry

Machine learning and artificial intelligence can lower cybersecurity’s wall of worry. Deploying solutions that can traverse massive amounts of data to produce usable results without human intervention lowers the wall. IT personnel do not have to worry that something is being missed.

IT departments can monitor end-user activity for stronger cybersecurity in remote work environments. The technology can learn behaviors such as when a specific end-user accesses the system and from where. AI can then alert the appropriate personnel for further investigation. It’s important to remember that internal bad actors exist.

Lowering the wall of worry removes a lot of stress across the enterprise. If IT is focused on reading logs and monitoring traffic, they are not helping internal staff or working on improvements. If you’re looking for ways to lower your wall, we are ready to discuss your needs. Contact us today to learn more.

Behind F33
Greg Bigos, CEO

Greg Bigos is the CEO of F33, bringing over decades of experience in delivering ERP solutions for manufacturing, logistics, and retail industries.

Contact Greg
Behind F33
Wit Jakuczun, CTO

Wit Jakuczun is the CTO/Chief Data Science Officer at F33 with a PhD in Applied Mathematics and over 18 years of experience in mathematical modeling, data analysis, and simulations.

Contact Wit

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