If you’re worried about hacks and security incidents at your company, your top concern shouldn’t necessarily be the ever-growing sophistication of hacking technology. You might be surprised to learn that hackers aren’t the sole reason for cybersecurity breaches. Instead, the people behind your computers may be a real threat to your business.
According to the 2016 Data Incident Response Report, human error is still to blame for most security breaches. An employee who doesn’t detect a phishing email as containing malware may inadvertently download a virus to their computer and expose their machine or the network to ransomware. Employees may also use the same easy-to-guess password for all of their systems, use public WiFi to read sensitive documents or fail to update their computer’s software systems even when prompted. While that’s never good news, it does mean that relying on machine learning and artificial intelligence (AI) as a means to help eliminate human error is well within reach.
Combining the two technologies can also help you rapidly detect, resolve, and prevent security threats before they spiral out of control and negatively impact your business. Machine learning and artificial intelligence can also help enhance the duties of cybersecurity experts and give them the resources they need to excel in their roles.
Here’s a look at how machine learning and artificial intelligence are shaping the future of cybersecurity.
As cyberattacks become more sophisticated, cybersecurity teams are tasked with adapting their technology to find new anomalies. It may sound simple in theory, but that also means cybersecurity experts would have to stay ahead of the hacking trends at every moment. And because hackers don’t work off of a universal malware script and are always experimenting, it’s nearly impossible to learn and look for every type of anomaly.
This is where machine learning and AI enter the picture. Cybersecurity experts need a reliable way to quickly scan, parse, and react to those anomalies. Once machine learning and AI learn what to look for, they can quickly give their human counterparts the information they need to mitigate attacks and the fallout.
It’s possible security teams may not need special assistance to find small anomalies. But detecting and addressing advanced, complex hacks and security issues is an uphill battle. In order to stop complex attacks, thousands of pieces of ever-changing data and anomalies need to be quickly analyzed in order to find potential incidents. This is a more scalable approach to stopping big attacks before they ever reach your business.
What machine learning and AI are actually delivering is an arsenal of knowledge and a blueprint for their human operators to work from. According to TechCrunch, using technology like machine learning and AI in cybersecurity allows operators to reverse engineer attacks and make improvements to their systems. This process keeps operators from simply working in reactive mode to every incident, and instead lets them figure out how to address and prevent them from happening again.
Identifying and parsing those anomalies and connections in a security incident is just the first step in combating hackers. It also takes a significant amount of time for cybersecurity experts to properly respond to an attack and figure out how to contain and destroy it before it escalates. And in many cases, it takes more time to respond to an incident than businesses have before their systems are hit.
Artificial intelligence can help engineers figure out how to approach the attack and learn what’s working, and how to apply those lessons to a future hack. That means the technology can use those lessons to stop a hack or alert a human operator on best practices to respond to an incident. This can dramatically shorten the response process and lessen the financial and reputational damage from a customer-facing hack.
Cybersecurity is complex with lots of moving pieces and requires a management component in order to keep your business safe. Once cybersecurity experts have all the information about an attack and how to address it, they still need help managing the process with an understanding of which steps to take first. It can take time for a human to assess and comprehend before passing on recommendations about how to mitigate an attack to other team members or supervisors.
Cybersecurity experts need technology to help manage this process. Virtualizing cybersecurity can help add a management layer on top of existing security tools and help break down security steps to find the appropriate tools for each issue. Once this information and management layer is in place, security experts can respond in an organized and efficient manner.
Detecting and resolving attacks are just two ways of combining machine learning and AI with cybersecurity can benefit companies. Businesses also need to learn about new vulnerabilities and resolve them before they become an issue in the first place. Delaying this type of learning just puts your business in the hands of hackers who are always looking for the latest vulnerabilities and leveraging them immediately.
And the technology hackers are using isn’t necessarily sophisticated. It could just be a matter of finding companies that haven’t bothered updating available security patches. According to the Verizon Data Breach Report, over 70% of attacks exploit known vulnerabilities that already have available patches.
And those patches aren’t necessarily very old. Hackers are known to jump on vulnerabilities immediately after they become public knowledge. This gives them the leverage to attack your business before anyone has had the time to catch up and start applying relevant patches and testing the results. Machine learning can look for these vulnerabilities and work to resolve them before they impact your business.
Humans can only detect trends as they first start to emerge. It often requires studying millions of data points and reading up on what other businesses are experiencing in security threats to put the pieces together. If cybersecurity experts are fortunate and highly skilled, they may be able to see connections well before the mainstream. However, once these trends are identified, it takes work to figure out how they can apply to your business’ security issues. This type of constant, in-depth monitoring takes both skill and time and is susceptible to human error, not to mention rapidly-changing hacking trends.
Meanwhile, machine learning can see subtle, complex anomalies at their earliest stages and track them as they evolve and change. It can also look at how these trends might unlock unseen vulnerabilities and create a domino effect of breaches, or connect them to other attacks to see the trends before humans can. Now artificial intelligence can step in and learn how to fix those breaches and make sure any resolutions are applied to future vulnerabilities.
For years, people have speculated whether artificial intelligence will take over humans’ roles and make them obsolete. But it’s unlikely humans can ever be replaced. Instead, AI can ultimately make teams superhuman by giving them tools that simplify complex and difficult cybersecurity issues. That gives humans the resources they need to apply that knowledge to their business decisions and ongoing security enhancements.
Although we don’t know how machine learning and artificial intelligence will advance, right now it can’t meet the needs of our businesses. These technologies aren’t present in our business meetings and are not applying shared knowledge from our team members. We’re still in charge of how to best combine cybersecurity with our business needs, and with the appropriate budget.
Cybersecurity costs are no longer an optional budget item for businesses serious about safeguarding their security. And the rising costs of cybersecurity show no signs of slowing down. According to reporting from CSO on Gartner research, cybersecurity spending will exceed $1 trillion from 2017 to 2021.
However, a more efficient cybersecurity process can help reduce costs and help streamline the process. Artificial intelligence and machine learning can rapidly and efficiently detect threats, resolve them, and prevent them in the shortest amount of time possible with the greatest potential for resolution.
AI and machine learning also help businesses avoid soaring costs associated with malware and hacks. For example, it’s not just the costs of repairing systems and lost productivity that damage your business in an attack: the loss of business reputation and revenue, and hidden expenses like rolling out a communication and PR team to handle the fallout, are costly.
The cybersecurity industry is poised to dive into machine learning and artificial intelligence. After all, the real goal of machine learning and artificial intelligence is to learn and enhance the role of human operators. And because the landscape of hacking technology and cybersecurity best practices is constantly changing, it requires that type of rapid-learning to make sure your business stays safe. But perhaps most importantly, if we don’t harness the power of cybersecurity and artificial intelligence first, hackers will eventually adopt the technology to make their own jobs easier.
Do you use machine learning and artificial intelligence in your own business to enhance your cybersecurity? Let us know about your experience by leaving a comment below:
Founder & Chief Marketing Officer, Indusface
Venky has played multiple roles within Indusface for the past 6 years. Before this, as the CTO @ Indusface, Venky created the product/service offering and technology team from scratch and grew it from ideation to getting initial customers with a proven/validated business model poised for scale. Before joining Indusface, Venky had 10+ years of experience in the security industry and had held various mgmt/leadership roles in Product Development, Professional Services, and Sales @Entrust.