The AI ​​edge in cybersecurity: Predictive tools aim to reduce response times

Modern cybersecurity professionals need advanced technologies to deter, detect, and repel hackers, and the predictive benefits of AI can mean the difference between data protection and ruin.

The average cost of a data breach in the US was a high water mark from $9.48 million in 2023. Losses have increased every year since 2013, even during the global health crisis of Covid-19, when many businesses had to close their doors. An analysis in IBM’s 2024 data breach report shows that organizations that implement comprehensive AI security automation have saved $2.22 millionwhile also reducing cybersecurity insurance.

Industry leaders would do well to think about cyber attacks beyond the financial implications. Should your organization pay a ransomware demand or right the ship after a crushing malware attack, the reputational damage could far outweigh the dollars. When hackers steal confidential, sensitive, and personal identity information, those around you are negatively affected. Employees, customers and industry partners can file civil lawsuits.

And if word gets out that your company can’t protect personal data, the business community can become eerily quiet. It is not unusual for an institution to file for bankruptcy within a year of a significant breach of trust. Fortunately, AI cybersecurity can strengthen your defenses and cause cybercriminals to look elsewhere for low-hanging fruit.

What role does AI play in cybersecurity?

There are far-reaching benefits to integrating AI into an operation’s cybersecurity posture. The long list, which we will briefly discuss here, has one central theme: response time. At the heart of the thought leadership behind the use of AI in the data protection sector is reducing the time it would otherwise take to detect and deter hackers.

The role AI plays in today’s lightning-fast hacking landscape could determine whether companies suffer major losses and setbacks or walk away unscathed. When you consider how quickly a sophisticated cybercriminal can work, it’s obvious why Time is on the side of the bad guys unless we do something about it.

• Ransomware attacks: These hacks typically last four hours, but advanced persistent threats can take over a corporate network in as little as 45 minutes. Ransomware attacks happen every 11 seconds.
• Phishing emails: Nearly 30% of all phishing emails are opened by the recipient. These malware-borne communications are responsible for 91% of all cyber attacks.
• Malware Deployment: Hackers are deploying malware at a rate of 11.5 attacks per minute.

It takes the average hacker just 9.5 hours to steal valuable and sensitive digital assets. Cybercriminals can operate with impunity if no one is monitoring operations while the business is closed and staff are fast asleep. Operations without AI, machine learning (ML) and other advanced technologies typically take an average of 197 days to detect a breach and another 67 days to mitigate it. Hackers like to hide in plain sight and copy incoming data until you delete it.

The benefits of using predictive AI technology

The fundamental element of AI in cybersecurity may be the effectiveness of time management. It is important to understand how this forward-looking technology benefits an organization’s overall cyber hygiene. Here are some ways AI delivers quantitative and qualitative data security benefits.

Advanced threat detection

AI’s ability to sift through vast amounts of data seemingly at the speed of light cannot be matched by humans. Programmed to learn and identify even subtle anomalies in network traffic, user activity and system logs can make it difficult for hackers to go unnoticed. By generating real-time and continuous analysis of far-reaching movements, anything that deviates from predictive patterns is flagged. A cybercriminal or deployed malicious software immediately triggers a threat detection alert. The most skilled perpetrator could not get the 45 minutes needed to effectively insert a ransomware file.

Behavioral analysis

To say that AI is exceeding expectations in behavioral analytics would be an understatement. ML, largely a subcategory of AI, involves tracking and understanding consistent patterns. For example, a legitimate network user enters a username, password, and then a two-factor authentication code. Once in the system, employees perform relatively consistent tasks. That means they open the same programs, access similar data, and perform these tasks in a uniform way.

When a hacker orchestrates an attack, the digital intruder is not interested in filing incident reports or mapping inventory. Cybercriminals are looking for valuable and confidential information that can be sold on the dark web. As AI and ML track user behavior – sometimes down to keyboard strokes – alarms are triggered and prompt actions are taken to mitigate and eliminate the threat.

Reduce error threat alerts

Before organizations adopted AI and ML, responding to false alarms seemed like the cost of doing business. This is largely because the alternative did not know when there was a real threat. In terms of efficiency, pre-AI threat detection was a lot like a fire department responding to dozens of alarms triggered by overly sensitive heat detectors.

The rise of AI has been a game changer when it comes to reducing false alarms and reducing the time IT and security officials spend vetting each alarm. As technology adapts to common false positives and learns to distinguish between low-level anomalies and serious anomalies, cybersecurity professionals spend fewer wasted hours.

Non-stop threat monitoring and learning

While humans and most machines require downtime, AI works relentlessly to identify anomalies. During this never-ending process, technology continues to collect useful information. It can adapt to changes in the digital landscape and be reconfigured to assess new standards. The alternative to AI would be to hire full-time staff and monitor system operations 24 hours a day, 7 days a week. For many organizations, the cost of non-stop threat monitoring can prove prohibitive.

Become familiar with AI automated incident response

One of the processes that AI enables includes automated responses to threats. Not every business leader is comfortable with technology mitigating threats, whether it’s malware, ransomware or a human attempting a blunt force attack. There is a certain loss of control that comes with letting the so-called ‘machines’ take over. But automated incident responses can be in your best interest.

Industry leaders can choose their comfort level with which threats are handled by the technology and which are brought to the attention of a real person. Low-level threats are typically managed by AI, and it is common for AI to begin mitigating threats while security professionals respond to an alert. These are among the benefits companies gain from automating a variety of incident responses.

• Speed ​​and efficiency: Predetermined responses to emerging threats occur immediately. The speed at which AI can address these issues helps efficiently mitigate risk.
• Minimize human errors: The majority of successful data breaches can be traced back to human error. Technologies such as AI and others carry out the procedures and tasks assigned to them. You can’t trick AI into giving users access to data that’s considered off-limits.

Integrating AI and ML can be one of the most cost-effective ways to strengthen your cybersecurity posture. It does the work of dozens of people faster and more efficiently without recording overtime. Adaptable to widespread networks and architectures such as zero trust, its ability to search vast amounts of data, identify patterns and continuously learn makes it invaluable in risk management. When a threat actor finds a way into your network or an insider tries to steal a trade secret, they cannot escape the watchful eye of AI.

John Funk is a creative advisor at SevenAtoms.