The traditional approach to preventing cyber-attacks has been to strengthen the perimeter in an effort to repel intruders. But it turns out that this boundary is not a wall but a sieve that hackers can breakthrough if they look in the right place.
However, cybersecurity can directly address these challenges when powered by Artificial Intelligence and machine learning solutions.
So it's no wonder that cyber-security experts believe that Machine Learning (ML) and Artificial Intelligence (AI) are changing the face of information security. The algorithms used in an AI automated cybersecurity assessment tool rely on training data that teaches them how to evaluate variables and respond under different circumstances.
You can also eliminate the need for human involvement in menial, repetitive tasks such as risk assessment and compliance.
Here's what you need to know about AI in cyber-security.
Current Cybersecurity Challenges
Company owners and IT professionals understand the growing concerns of dangerous cyber-attacks. Modern cyber-security developments are designed to counter challenges such as:
- Geographical distance. IT systems located in a different place make it more challenging to track cyber-security threats and incidents manually. Cyber-security professionals, therefore, have to develop strategies that counter infrastructural differences.
- Growing expenses. Manually hunting threats is time-consuming and expensive. IT professionals need to rely on an automated cyber-security assessment tool that can immediately detect threats.
- Sophisticated cyber-threats by hackers. Many organizations don't invest in the predictive cyber-security technology they need. Instead, they rely on a reactive system that fills security gaps after a threat has been encountered.
- Increasing complexity of cybersecurity regulations. As the world has become more and more digitized, so have more regulatory bodies increased the necessary regulations that businesses must comply with. Many organizations simply cannot shoulder these new regulations alongside managing their own unique inherent risk profile.
- Cyber-attackers' anonymity. Cyber-attackers ensure they remain anonymous and undetected by using proxies and VPNs. AI's training data enables it to track these risks.
The Need For AI In Cybersecurity
All cyber-security organizations need advanced threat detection methods that can minimize the risks of breaches or identity thefts. A data breach can result in an irreparable reputational loss for your company, damaging your relationship with partners and clients alike.
The Federal Trade Commission and Consumer Financial Protection Bureau ensure that any company which doesn't adequately protect consumer privacy will compensate for the damage. Therefore, data breaches can also be extremely expensive.
An example is the 2017 data breach that the credit bureau Equifax went through. They compromised 147 million clients' data and had to pay a global settlement of up to $425 million. Norton reports that it takes 196 days for a company to recover from a data breach on average.
AI in cyber-security helps you avoid these monetary and reputational consequences. IT and cyber-security professionals can leave threat intelligence to AI while investing their energies in mitigating risks that require human intervention.
ML and AI recognize data patterns and detect threats based on past incidents and risks. An automated AI system also decreases incident response time, thereby making your company more compliant and its cyber-security more robust. Here are a few ways AI improves cyber-security systems.
Cyber-attackers are becoming increasingly sophisticated in their efforts. There are new threats that companies have to face each day. However, a traditional cyber-security system relies on indicators or signatures to detect threats it has already encountered in the past. Therefore, this method doesn't work for new, undiscovered threats.
A study that surveyed 850 executives found that 61% of them maintain the opinion that they wouldn't be able to keep company data secure without AI. Methods that rely on signatures can recognize around 90% of threats. Overhauling your current system and relying exclusively on AI will up that percentage to 95%, but you'll have to work through a slew of false positives.
It's best to integrate an automated cyber-security assessment tool into your existing system so you can enjoy the security benefits of both.
A study from the Ponemon Institute reports that unpatched vulnerabilities cause 60% of breaches. Organizations using large systems such as SAP can't keep up with the vulnerabilities they have to patch, so they prioritize ones that present the highest risk.
However, AI can analyze a wide range of accounts and servers to detect anomalies before a vulnerability is even reported. An example of such an ML method is known as User and Behavioral Analytics.
Risk And Compliance
AI makes it possible to conduct detailed, comprehensive risk analysis for all types of threats. Since ML relies on past patterns, your cyber-security system will be able to take many different variables into account and analyze them quickly.
AI can provide you with in-depth risk analysis taking factors that traditional cyber-security systems don't even consider. These could include predictive variables such as market movements and the day's news.
AI and ML combined can assess real-time information to alert you to suspicious activity by detecting patterns in the organization's database. AI audit trails also enable you to counteract fraud and ensure compliance. You can check logs to see when employees have signed in or out of the system and every action they've taken. This comprehensive data collection allows you to recognize anomalies quickly and resolve them.
Industries such as IT, banking, and healthcare have strict regulations that they need to adhere to. As the complexity of the compliance landscape has increased, organizations like the Federal Financial Institutions Examination Council (FFIEC) developed the cybersecurity profile for the financial services sector (Financial Services Sector Cybersecurity Profile) as a means to support a common language across the growing number of standards financial services organizations must adhere to. There is another method, though. As we have seen with the cybersecurity skills shortage, oftentimes the required human capital to support the growing requirements for risk and compliance teams from internal stakeholders, the Board of Directors, and external regulatory bodies simply are not available. Instead of employing a team and dispensing resources to create detailed reports that prove compliance, you can use AI. An automated cyber-security assessment tool automatically generates extensive reports that you can forward to regulatory bodies, thereby saving you time and money.
Improving Compliance Through AI
Automated cyber-security assessment tools are useful at tasks that require repetitive processes on large data sets—such as risk documentation and compliance automation.
Automation enables people to focus on tasks that require their attention instead of wasting time and effort on manual threat hunting and menial data-related duties.
CyberStrong is a risk and compliance assessment tool that can automate workflows, collect data, and report—thereby removing the need for human intervention. You can secure your organization against threats while saving time and money.
Contact us today to learn more about CyberStrong—the cyber risk automation solution that augments and enables fully automated risk and compliance assessments.