How Effective Is AI for Cybersecurity Teams? 2025 Statistics

In 2025, it’s estimated that for every employee in an organization there are up to 200 attack vectors cybercriminals could exploit. 

Phishing, malware, and deepfake attacks are leveraging adversarial AI to become more effective. Bring-your-own-device (BYOD), shadow IT, and IoT devices continue to change the playing field for IT teams. Cloud applications and third-party vendors improve workflows for users, but can lead to security challenges too. 

And all of those factors add up to produce a record amount of data. Users and devices are on pace to create 79 zettabytes of information this year.

It’s become almost impossible for legacy cybersecurity systems to keep pace with the technological changes and influx of information. Many IT teams are turning to AI as a cybersecurity solution.

But using AI for security doesn’t come without its own risks and costs. Let’s take a closer look at the latest trends and stats to see how cybersecurity teams can most effectively leverage AI.

AI & Cybersecurity Statistics: Editor’s Picks

AI and cybersecurity are coming together to change the way organizations handle threats. With the rise in cyberattacks and large data volumes, AI is now essential. It helps make security measures quicker and more precise.

Here are key stats showing how AI affects cybersecurity. They highlight new opportunities and challenges for IT teams:

• 80% of cybersecurity pros believe AI is beneficial to security compared to 20% who are worried about the risks of deploying AI in cybersecurity.
• 85% of IT stakeholders believe the only way to stop AI-generated threats is through the use of AI-driven cybersecurity solutions.
• An IBM survey found that 67% of organizations are currently using AI as part of their cybersecurity strategy, with 31% relying on AI extensively.
• IBM’s Threat Detection and Response Service utilizes AI tools to actively monitor over 150 billion security events every day. 
• Cloud security, data security, and network security are the top three areas where AI security solutions are projected to have the biggest impact.
• The costs associated with cybercrime are expected to soar over $10 trillion in 2025, a 300% increase in the last decade.
• The market for AI in cybersecurity is projected to reach over $60 billion by 2028, an increase of 170% from 2023.
• However, 84% of cybersecurity stakeholders are still concerned about data quality and privacy issues when training AI for security applications.

Where and How Is AI Being Used in Cybersecurity

Integrating AI-powered tools into existing security frameworks is a top priority for many companies. Enhancing legacy systems with AI isn’t always easy. Almost 90% of security professionals prefer a platform approach to a collection of individual security tools. 

Cybersecurity teams are leveraging AI to streamline detection and speed up response times. AI tools can spot anomalies and flag threats in real time, stopping attacks before they escalate. Automated responses shut down risks in seconds, minimizing vulnerability. AI systems also reduce false positives, easing the workload on human teams so they can focus on critical issues.

AI makes it possible to use advanced authentication methods like facial recognition and CAPTCHA. 

Machine learning (ML) helps AI security systems quickly adapt to new threats. It filters out bots, spam, fraud, and phishing attempts. Plus, it can spot unknown threats that human teams might overlook. 

AI’s Effectiveness: Key Statistics

When it comes to preventing threats, AI models have proven to be more effective than legacy systems. This happens in places that need fast data processing. It’s especially true for stopping AI-generated attacks. Let’s look to the numbers that prove how AI enhances cybersecurity efforts.

Threat Detection and Response

AI speeds detection and response times from days to minutes and hours to seconds. With hackers on the constant hunt for new vulnerabilities, AI gives cybersecurity teams the ability to close gaps faster and prevent catastrophic problems. 

• 74% of IT security pros say AI-powered attacks pose a significant threat to their organization’s operations.
• A Ponemon Institute study revealed that 70% of cybersecurity pros say AI is highly effective for identifying threats that otherwise would have gone undetected.
• On average, organizations with fully deployed AI threat detection systems contained breaches within 214 days. Organizations that relied on legacy systems took 322 days to effectively perform the same operations.
• The latest data shows that AI improves threat detection by 60%.
• 64% of organizations deploy AI for threat detection. 
• A 2023 study revealed that some AI-security tools improved incident detection and response times from an average of 168 hours to only seconds.

Phishing Prevention

Bad actors use AI to craft personalized and convincing phishing messages. They also make malware links harder to spot. Using AI-powered security tools helps to identify and filter out those threats with more accuracy.

• A study by the Harvard Business Review found that criminals cut the costs of creating phishing emails by 95% by using LLMs, while achieving equal or greater success in getting end users to fall for phony emails.
• Deep Instinct’s security pros found that AI-driven tools prevent phishing at a 92% rate compared to 60% for legacy systems.
• Research at Cornell University demonstrated that browser extensions equipped with machine learning capabilities effectively detected over 98% of phishing attempts, a much better rate than browser or web-based methods without AI.
• AI tools developed by Microsoft analyzed data from trillions of security signals from 40 countries and 140 known hacker groups and have stopped over 35 billion phishing attacks.

Cost Savings

Using AI in cybersecurity can save organizations money. It speeds up incident response for human teams and reduces financial losses from security breaches.

• IBM’s Cost of a Data Breach report calculated that companies using AI for prevention netted a nearly 50% reduction in costs compared to organizations that did not use AI, an average savings of over $2 million.
• Detection, investigation, and response times were the top three areas where AI was most effective compared to legacy systems.
• In 2023, Visa prevented $40 billion worth of fraudulent transactions through the use of AI-driven cybersecurity systems.

Challenges of AI in Cybersecurity

Many organizations see AI cybersecurity systems as still new. This means they will face some challenges along the way. While AI streamlines security protocols, there is a learning curve for both human-led teams and ML models.

• 65% of companies report at least some issue integrating AI security solutions with legacy systems.
• Only 26% of IT pros have a complete understanding of how AI is being used in security. 
• 86% of security professionals do not think generative AI alone is enough to stop emerging and zero-day threats.
• Almost one-third of organizations need cybersecurity vendors to explain how AI is being used in the solutions they offer.

False Positives

AI improves threat detection most of the time. However, it can also lead to false positives. This happens when it misreads the training data. False positives can tire out IT staff and cause distractions. This drains valuable resources from organizations. 

• 58% of security pros said it took longer to determine a threat was a false positive than to fix a true positive.
• 72% of security teams believe that false positives have a negative effect on team productivity.
• Many studies show that over 50% of security teams ignore alerts that turn out to be false positives. This can lower their overall effectiveness against real threats.

Adversarial AI

Malicious actors have learned to use adversarial AI to create increasingly sophisticated cyberattacks.

Attacks are more individual, easier to generate, and harder for human end users to recognize. AI security tools usually spot new threats better than older systems and humans. However, the ongoing flow of new attacks can still compromise security at any moment.

• Since 2022, phishing emails and credential phishing have increased by about 1,000%.
• By 2027 losses from deepfakes and similar attacks are expected to reach $40 billion annually.
• Large enterprises like Amazon are facing nearly 1 billion cyber threats daily, due in part to the widespread proliferation of adversarial AI.

Implementation Costs

Adding AI to current security systems can take a lot of time and resources for many cybersecurity teams. Using AI in security systems can save money in the long run. However, the upfront cost may be high. This is especially true for large organizations with old systems that need updates. 

• The Ponemon Institute discovered that only 44% of stakeholders could accurately determine how to deploy AI security tools most cost effectively within their organization.
• The same study revealed that 54% of organizations needed to hire external experts in order to maximize the benefits of AI-powered security technology.
• 65% of security teams reported challenges when integrating AI security solutions with legacy systems.
• 61% of enterprise security teams found it difficult to find AI-based security controls that could be deployed across their entire organization.
• The top two reasons organizations have not adopted AI in cybersecurity are insufficient budget and lack of internal expertise.

Future Predictions

AI use cases in cybersecurity are growing fast. Cybercriminals are also using AI for harmful activities. Many cybersecurity teams use AI to track the increasing number of devices. 

They also manage the vast amounts of data generated by users.

Machine learning algorithms are also improving every day. They identify threats more accurately and detect breaches in a fraction of the time as legacy systems. Cybersecurity teams see the value of machine learning in SOAR (security orchestration, automation and response). It helps automate responses. This cuts down on time and reduces the need for human help. 

Security teams are also exploring the benefits of predictive analytics. By using ML to analyze data, they can identify vulnerabilities and prevent cyberattacks before they happen. Innovators in the industry are using adversarial AI to mimic real-world attacks. This helps test how well AI defenses work. 

AI and ML are effective against cyberattacks. However, training these systems raises ongoing questions about data use and user privacy. 

AI tools are emerging as a cybersecurity solution. As they grow in use, cybersecurity teams should expect new regulations and standards to follow. IT pros will also need to stay vigilant, to ensure that AI security systems themselves are not exploited by hackers.

It won’t be long before AI is a standard part of every cybersecurity system. By staying on top of the latest developments, you’ll find the most effective way to use AI for your cybersecurity team.

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