What is AI security?

Organizations can incorporate AI into cybersecurity practices in many ways. The most common AI security tools use machine learning (ML) and deep learning to analyze vast amounts of data, including traffic trends, app usage, browsing habits and other network activity data.

This analysis allows AI to discover patterns and establish a security baseline. Any activity outside that baseline is immediately flagged as an anomaly and potential cyberthreat, allowing for swift remediation.

AI security tools also frequently use generative AI (gen AI), popularized by large language models (LLMs), to convert security data into plain text recommendations, streamlining decision-making for security teams.

Research shows that AI security tools significantly improve threat detection and incident response. According to the IBM Cost of a Data Breach Report, organizations with extensive security AI and automation identified and contained data breaches 108 days faster on average than organizations without AI tools.

Also, the report found that organizations that extensively use AI security save, on average, USD 1.76 million on the costs of responding to data breaches. That’s an almost 40% difference compared to the average cost of a breach for companies that do not use AI.

For these reasons, investment in AI security is growing. A recent study projected that the AI security market, valued at USD 20.19 billion in 2023, will reach USD 141.64 billion by 2032, growing at 24.2% annually.¹

AI security means different things in different contexts. While the focus of this page is the use of AI to improve cybersecurity, two other common definitions center on securing AI models and programs from malicious use or unauthorized access.

In one definition, cybersecurity experts focus on how threat actors are using AI to improve their cyberattacks or exploit new attack surfaces.

For instance, LLMs can help attackers create more personalized and sophisticated phishing attacks. Being a relatively new technology, AI models also provide threat actors with new opportunities for cyberattacks, such as supply chain attacks and adversarial attacks (see “Potential vulnerabilities and security risks of AI”).

Another definition of AI security is about securing AI deployments. A recent study from the IBM Institute for Business Value found that only 24% of current gen AI projects are secured. Securing AI projects involves practices to secure AI data, models and usage.

For example, red team exercises—where ethical hackers behave as if they are real-world adversaries—commonly target AI systems, machine learning models and datasets that support AI and ML applications.

Securing AI deployments may also involve monitoring to root out cases of shadow AI, which is the unsanctioned use of AI models and tools. All these practices also function to help businesses manage AI risk and address compliance issues.

Today's cyberthreat landscape is complex. The shift to cloud and hybrid cloud environments has led to data sprawl and expanded attack surfaces while threat actors continue to find new ways to exploit vulnerabilities. At the same time, cybersecurity professionals remain in short supply, with over 700,000 job openings in the US alone.²

The result is that cyberattacks are now more frequent and more costly. According to the Cost of a Data Breach Report, the global average cost to remediate a data breach in 2023 was USD 4.45 million, a 15% increase over three years.

AI security can offer a solution. By automating threat detection and response, AI makes it easier to prevent attacks and catch threat actors in real time. AI tools can help with everything from preventing malware attacks by identifying and isolating malicious software to detecting brute force attacks by recognizing and blocking repeated login attempts.

With AI security, organizations can continuously monitor their security operations and use machine learning algorithms to adapt to evolving cyberthreats.

Not investing in AI security is expensive. Organizations without AI security face an average data breach cost of USD 5.36 million, which is 18.6% higher than the average cost for all organizations.

Even limited security might provide significant cost savings: Those with limited AI security reported an average data breach cost of USD 4.04 million—USD 400,000 less than the overall average and 28.1% less than those with no AI security at all.

Despite its benefits, AI poses security challenges, particularly with data security. AI models are only as reliable as their training data. Tampered or biased data can lead to false positives or inaccurate responses. For instance, biased training data used for hiring decisions can reinforce gender or racial biases, with AI models favoring certain demographic groups and discriminating against others.³

AI tools can also help threat actors more successfully exploit security vulnerabilities. For example, attackers can use AI to automate the discovery of system vulnerabilities or generate sophisticated phishing attacks.

According to Reuters, the Federal Bureau of Investigation (FBI) has seen increased cyber intrusions due to AI.⁴ A recent report also found that 75% of senior cybersecurity professionals are seeing more cyberattacks, with 85% attributing the rise to bad actors using gen AI.⁵

Moving forward, many organizations will look for ways to invest time and resources in secure AI to reap the benefits of artificial intelligence without compromising on AI ethics or security (see "AI security best practices").

AI capabilities can provide many advantages in enhancing cybersecurity defenses. Some of the most significant benefits of AI security include:

• Enhanced threat detection: AI algorithms can analyze large amounts of data in real-time to improve the speed and accuracy of detecting potential cyberthreats. AI tools can also identify sophisticated attack vectors that traditional security measures might miss.

• Faster incident response: AI can shorten the time needed to detect, investigate and respond to security incidents, allowing organizations to address threats more quickly and reduce potential damage.

• Greater operational efficiency: AI technologies can automate routine tasks, streamlining security operations and cutting costs. Optimizing cybersecurity operations can also reduce human error and free security teams for more strategic projects.

• A proactive approach to cybersecurity: AI security enables organizations to take a more proactive approach to cybersecurity by using historical data to predict future cyberthreats and identify vulnerabilities.

• Understanding emerging threats: AI security helps organizations stay ahead of threat actors. By continuously learning from new data, AI systems can adapt to emerging threats and ensure that cybersecurity defenses stay current against new attack methods.

• Improved user experience: AI can enhance security measures without compromising user experience. For example, AI-powered authentication methods, such as biometric recognition and behavioral analytics, can make user authentication more seamless and more secure.

• Automated regulatory compliance: AI can help automate compliance monitoring, data protection and reporting, ensuring that organizations consistently meet regulatory requirements.

• Ability to scale: AI cybersecurity solutions can scale to protect large and complex IT environments. They can also integrate with existing cybersecurity tools and infrastructure, such as security information and event management (SIEM) platforms, to enhance the network's real-time threat intelligence and automated response capabilities.

Despite the many benefits, the adoption of new AI tools can expand an organization’s attack surface and present several security threats.

Some of the most common security risks posed by AI include:

AI systems rely on datasets that might be vulnerable to tampering, breaches and other attacks. Organizations can mitigate these risks by protecting data integrity, confidentiality and availability throughout the entire AI lifecycle, from development to training and deployment.

Threat actors can target AI models for theft, reverse engineering or unauthorized manipulation. Attackers might compromise a model's integrity by tampering with its architecture, weights or parameters—the core components determining an AI model's behavior and performance.

Adversarial attacks involve manipulating input data to deceive AI systems, leading to incorrect predictions or classifications. For instance, attackers might generate adversarial examples that exploit vulnerabilities in AI algorithms to interfere with the AI models' decision-making or produce bias.

Similarly, prompt injections use malicious prompts to trick AI tools into taking harmful actions, such as leaking data or deleting important documents.

If security teams don’t prioritize safety and ethics when deploying AI systems, they risk committing privacy violations and exacerbating biases and false positives. Only with ethical deployment can organizations ensure fairness, transparency and accountability in AI decision-making.

Adhering to legal and regulatory requirements is essential to ensuring the lawful and ethical use of AI systems. Organizations must comply with regulations such as the General Data Protection Regulation (GDPR), California Consumer Privacy Act (CCPA) and the EU AI Act or risk exposing sensitive data and facing heavy legal penalties.

Input manipulation attacks involve altering input data to influence the behavior or outcomes of AI systems. Attackers might manipulate input data to evade detection, bypass security measures or influence decision-making processes, which can lead to biased or inaccurate results.

For example, threat actors can compromise an AI system’s outputs in data poisoning attacks by intentionally feeding the model bad training data.

Supply chain attacks occur when threat actors target AI systems at the supply chain level, including at their development, deployment or maintenance stages. For instance, attackers might exploit vulnerabilities in third-party components, software libraries or modules used in AI development, leading to data breaches or unauthorized access.

AI models can experience drift or decay over time, leading to degraded performance or effectiveness. Adversaries can exploit the weaknesses in a decaying or drifting AI model to manipulate outputs. Organizations can monitor AI models for changes in performance, behavior or accuracy to maintain their reliability and relevance.

Applications of AI in cybersecurity are diverse and continually evolving as AI tools become more advanced and accessible.

Some of the most common use cases of AI security today include:

Data protection involves safeguarding sensitive information from data loss and corruption to protect data and ensure its availability and compliance with regulatory requirements.

AI tools can help organizations improve data protection by classifying sensitive data, monitoring data movement and preventing unauthorized access or exfiltration. AI can also optimize encryption and tokenization processes to protect data at rest and in transit.

Additionally, AI can automatically adapt to the threat landscape and continuously monitor for threats around the clock, allowing organizations to stay ahead of emerging cyberthreats.

Endpoint security involves safeguarding endpoints, such as computers, servers and mobile devices, from cybersecurity threats.

AI can improve existing endpoint detection and response (EDR) solutions by continuously monitoring endpoints for suspicious behavior and anomalies to detect real-time security threats.

Machine learning algorithms can also help identify and mitigate advanced endpoint threats, such as file-less malware and zero-day attacks, before they cause harm.

AI can help protect sensitive data across hybrid cloud environments by automatically identifying shadow data, monitoring for abnormalities in data access and alerting cybersecurity professionals to threats as they happen.

Threat-hunting platforms proactively search for signs of malicious activity within an organization's network.

With AI integrations, these tools can become even more advanced and efficient by analyzing large datasets, identifying signs of intrusion and enabling quicker detection and response to advanced threats.

As cyberattacks and identity theft become more common, financial institutions need ways to protect their customers and assets.

AI helps these institutions by automatically analyzing transactional data for patterns indicating fraud. Additionally, machine learning algorithms can adapt to new and evolving threats in real-time, allowing financial providers to continuously improve their fraud detection capabilities and stay ahead of threat actors.

AI security tools are often most effective when integrated with an organization’s existing security infrastructure.

For example, security orchestration, automation and response (SOAR) is a software solution that many organizations use to streamline security operations. AI can integrate with SOAR platforms to automate routine tasks and workflows. This integration can enable faster incident response and free security analysts to focus on more complex issues.

Identity and access management (IAM) tools manage how users access digital resources and what they can do with them. Their goal is to keep out hackers while ensuring that each user has the exact permissions they need and no more.

AI-driven IAM solutions can improve this process by providing granular access controls based on roles, responsibilities and behavior, further ensuring that only authorized users can access sensitive data.

AI can also enhance authentication processes by using machine learning to analyze user behavior patterns and enable adaptive authentication measures that change based on individual users’ risk levels.

LLMs like ChatGPT have made phishing attacks easier to conduct and harder to recognize. However, AI has also emerged as a critical tool for combating phishing.

Machine learning models can help organizations analyze emails and other communications for signs of phishing, improving detection accuracy and reducing successful phishing attempts. AI-powered email security solutions can also provide real-time threat intelligence and automated responses to catch phishing attacks as they occur.

Vulnerability management is the continuous discovery, prioritization, mitigation and resolution of security vulnerabilities in an organization’s IT infrastructure and software.

AI can enhance traditional vulnerability scanners by automatically prioritizing vulnerabilities based on potential impact and likelihood of exploitation. This helps organizations address the most critical security risks first.

AI can also automate patch management to reduce exposure to cyberthreats promptly.

To balance AI’s security risks and benefits, many organizations craft explicit AI security strategies that outline how stakeholders should develop, implement and manage AI systems.

While these strategies necessarily vary from company to company, some of the commonly used best practices include: