As today’s organizations struggle to protect themselves from the growing number of cyber threats, artificial intelligence (AI) and machine learning are poised to help them enhance their security posture.

AI and machine learning makes it possible to learn about and analyze potential cyber threats in real-time. And they employ algorithms to build models of behaviors, using these models to make predictions about cyber attacks as new data emerges. Together, these technologies are helping companies improve their security defense by increasing the speed and accuracy of their cybersecurity response.

An effective tool for combatting cyber attacks.

As more businesses make the digital transformation, cyber-attacks have been proliferating. In the U.S. for example, 2021 has been a record-breaking year, with the number of data breaches by the end of the third quarter exceeding all of 2020 by 17%, according to the Identity Theft Research Center.[i] At the same time, ransomware attacks have been increasing at an alarming rate, with the average incident costing companies more than $700,000.[ii] Today, ransomware attacks are happening every 11 seconds,[iii] resulting in an average 21 days of business downtime.[iv]

As hackers launch increasingly sophisticated attacks to bring down corporate networks, AI and machine learning can defend against these advanced attacks. Indeed, these technologies are quickly becoming mainstream tools for cybersecurity specialists in their ongoing battle to defeat bad actors.

According to a report by Capgemini Research Institute, 61% of organizations say they will not be able to identify critical threats without AI, while 69% believe AI will be necessary to respond to cyberattacks.[v] Indeed, the market for AI in cybersecurity is expected to grow to $46.3 billion by 2027.[vi]

As organizations plan their use of AI and machine learning in cybersecurity, here are three ways these technologies can help:

  1. Detecting anomalies: AI and machine learning use behavioral analysis and continuously evolving parameters to identify anomalies that may be indicative of an attack.
  1. Predicting future data breaches: AI and machine learning makes it possible to process large amounts of data of different types to predict cyber threats before they occur.
  1. Responding to data breaches in real-time: AI and machine learning can send alerts when a cyber threat is detected or respond autonomously without human intervention by automatically creating defensive patches immediately when an attack is detected.

Benefits of AI and machine learning.

Organizations that incorporate AI and machine learning into their cybersecurity program are reaping substantial benefits. These include:

  • Increasing the speed of detection and response: AI and machine learning can easily analyze massive amounts of data in seconds, making it far faster than manually detecting threats. What’s more, they can implement patches and remediate threats in near real-time, dramatically improving response times. With the ability of today’s cyberattacks to quickly penetrate an organization’s infrastructure, razor fast detection and response is key to success.
  • Lowering IT costs: AI and machine learning reduce the effort required to detect and respond to cyber threats, making them cost-effective technologies. The average cost reduction is 12%, with some organizations lowering their costs by more than 15%, according to the Capgemini report.[vii]
  • Increasing cyber analyst effectiveness:  AI and machine learning reduce the workload for cyber analysts by decreasing the time required to manually sift through data logs. These technologies can alert cyber analysts about an attack while classifying the type of attack—better preparing them to make the right response. With an ongoing, comprehensive analysis of behavior patterns, cyber analysts are better equipped to manage even the most complex threats with less manual effort.
  • Improving your overall security posture: With AI and machine learning, cybersecurity gets stronger over time as more data is analyzed and these technologies learn from past patterns to become more proficient at identifying suspicious activity. They also protect an organization’s infrastructure at both the macro and micro levels, creating more effective barriers than can be achieved using manual methods.

Concerns about AI and machine learning in cybersecurity.

While AI and machine learning are a critical part of a modern cybersecurity strategy, they also pose organizational risks. Without access to extensive data sets, for example, AI is more likely to generate false positives—blocking legitimate customers, employees, and other users from completing legitimate tasks.

In addition, hackers take advantage of AI and machine learning to improve their malware over time. They often use existing AI tools to more effectively attack traditional security systems, sometimes even bypassing AI-enhanced cybersecurity defenses.

Potential uses.

While AI and machine learning aren’t free of risk, their use is only expected to improve over time. Already, these technologies have proven to be highly effective in a wide range of use cases. Among the common use cases where companies are putting AI and machine learning to effective use include:

  • Scoring network risk: Many organizations are using AI and machine learning to rank the risks posed by different segments of their network. Machine learning is being used to analyze prior cyber threat data sets to find out which parts of the network have been most heavily targeted. It’s also being used to determine which parts of the network if compromised, would leave the company most severely damaged. With a score assigned to each part of the business network, cyber analysts are prioritizing their resources to focus on the highest risks.
  • Rapidly detect intrusions: Companies are also using AI to obtain automatic and precise insights into malicious activity. With machine learning’s ability to detect, analyze, and defend against cyber attacks in real-time, organizations are responding to intrusions as soon as they occur.
  • Identifying suspicious behaviors: AI and machine learning are also being used to pinpoint suspicious user behavior. By tracking the unexpected actions of users, such as workers logging in at unusual times of the day or making an unusually large number of downloads, organizations are using machine learning to distinguish between normal versus aberrant behavior that might be the sign of a cyber attack so they can address vulnerabilities before a data breach occurs.
  • Detecting fraud: Many companies are protecting themselves from financial fraud by using machine learning algorithms to predict unusual customer behavior. With the ability to quickly recognize if customer behavior is out of the ordinary, these technologies are helping businesses to detect possible fraud threats before they occur, reducing their financial losses.
  • Discovering malware: AI and machine learning are also helping organizations to predict future malware infections. Using patterns found in previous malware, machine learning enables cyber analysts to predict malware attacks and remediate the risk with a speed unattainable using manual processes.

Planning your implementation.

Incorporating AI and machine learning into one’s cybersecurity strategy can be challenging, and many organizations find it difficult to know where to start. Here are a few tips to help you reap the best results as you begin your implementation plan:

  • Hire qualified cyber analysts who are intimately familiar with the use of AI and machine learning, and determine which tasks you plan to automate and which you prefer to have humans manage. 
  • Make sure you have the data sets you need to begin using AI algorithms, that this data is complete and up-to-date, and that it’s well integrated with your infrastructure and applications.
  • Start with just one or two use cases that are easy to implement and offer tangible benefits for your organization, and set clear measurements of success for these test projects.
  • Develop a clearly defined workflow to help you validate, prioritize, and analyze potential threats.
  • Create control processes to help you identify if an AI algorithm isn’t behaving as expected so you can rapidly troubleshoot any issues. 
  • Evaluate the results of your test projects and implement any necessary changes as you incorporate AI and machine learning into other parts of your cybersecurity strategy.

Powerful tools for an escalating problem.

As cyber-attacks increase in both volume and sophistication, AI and machine learning are powerful technologies that can help organizations be more prepared. With the right tools in place, your organization can detect and respond to cyber attacks in real-time, while remediating potential threats before they turn into serious issues. The result is faster detection, lower costs, and an improved security posture that enables you to keep up with the speed and scale of today’s risks.

As you work to strengthen your cybersecurity strategy, SailPoint can help you obtain better results. Find out how SailPoint’s comprehensive identity platform employs rich machine learning and autonomous risk detection and mitigation to help companies dynamically protect their cloud enterprises from the most advanced cyber attacks.

[i] Identity Theft Resource Center to Share Latest Data Breach Analysis with U.S. Senate Commerce Committee; Number of Data Breaches in 2021 Surpasses all of 2020 – Identity Theft Resource Center (idtheftcenter.org)

[ii] Understanding the true, hidden costs of ransomware attacks on the business (acronis.com)

[iii] Global Ransomware Damage Costs Predicted To Reach $20 Billion (USD) By 2021 (cybersecurityventures.com)

[iv] Ransomware Payments Decline in Q4 2020 (coveware.com)

[v] AI-in-Cybersecurity_Report_20190711_V06.pdf (capgemini.com)

[vi] Artificial Intelligence (AI) in Cybersecurity Market | Meticulous Market Research Pvt. Ltd. (meticulousresearch.com)

[vii] AI-in-Cybersecurity_Report_20190711_V06.pdf (capgemini.com)

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