AI for security involves using artificial intelligence to enhance and automate security processes, such as threat detection, incident response, and vulnerability management. Leveraging machine learning and data analytics, AI can identify patterns and anomalies that indicate potential threats. It can also significantly reduce analyst time spent on manual tasks - correlating incidents, summarizing resolution notes, and speeding up investigations.
Artificial intelligence (AI) has the capacity to revolutionize nearly every aspect of modern business—including cybersecurity.
AI is transforming the cybersecurity landscape by providing advanced tools and techniques to combat increasingly sophisticated threats. AI for security leverages machine learning, data analytics, and intelligent automation to enhance threat detection, improve incident response, apply intelligent analytics to predict possible risks, and streamline security operations. Industrial settings—such as factories and power plants—are especially vulnerable, due to their reliance on interconnected operational technology (OT) systems, making them ideal targets for those who might want to disrupt critical infrastructure. As AI becomes increasingly capable and available for both attack and defense purposes, it’s crucial that businesses utilize AI’s invaluable defenses to protect the systems and data they depend on.
AI is transforming the cybersecurity landscape by providing advanced tools and techniques to combat increasingly sophisticated threats. AI for security leverages machine learning, data analytics, and intelligent automation to enhance threat detection, improve incident response, apply intelligent analytics to predict possible risks, and streamline security operations. Industrial settings—such as factories and power plants—are especially vulnerable, due to their reliance on interconnected operational technology (OT) systems, making them ideal targets for those who might want to disrupt critical infrastructure. As AI becomes increasingly capable and available for both attack and defense purposes, it’s crucial that businesses utilize AI’s invaluable defenses to protect the systems and data they depend on.
It is important to recognize that AI for security is not the same thing as AI security.
AI for security
AI for security describes the use of AI technologies to enhance cybersecurity tools, processes, and solutions. It applies intelligent automation, predictive analytics, machine-learning capabilities, and more to create an efficient and reliable shield against even the most insidious attacks. AI for security also helps streamline and direct security response actions, allowing teams to respond more quickly to any anomalous system traffic or behavior that could indicate an in-progress attack, and to effectively mitigate the negative impact of these threats. These solutions are also able to learn from experience, becoming more effective over time.
AI security
While AI for security applies intelligent solutions to protecting digital networks and sensitive company and customer data, AI security simply refers to any security measures that may be used to protect the AI systems themselves. This can range from standard IT security solutions to advanced AI for security—effectively applying AI to help protect AI. AI security also involves addressing vulnerabilities within the AI development lifecycle.
AI for security describes the use of AI technologies to enhance cybersecurity tools, processes, and solutions. It applies intelligent automation, predictive analytics, machine-learning capabilities, and more to create an efficient and reliable shield against even the most insidious attacks. AI for security also helps streamline and direct security response actions, allowing teams to respond more quickly to any anomalous system traffic or behavior that could indicate an in-progress attack, and to effectively mitigate the negative impact of these threats. These solutions are also able to learn from experience, becoming more effective over time.
While AI for security applies intelligent solutions to protecting digital networks and sensitive company and customer data, AI security simply refers to any security measures that may be used to protect the AI systems themselves. This can range from standard IT security solutions to advanced AI for security—effectively applying AI to help protect AI. AI security also involves addressing vulnerabilities within the AI development lifecycle.
See how IT security automation, orchestration and collaboration can provide a healthy defense against the most serious cyberthreats.
AI for security works by integrating advanced algorithms into existing cybersecurity infrastructures. These algorithms make it possible for systems to analyze vast amounts of relevant data from many different sources, discovering and classifying patterns in the data to establish a baseline of ‘normal’ network and user behavior. With this foundation, it can then compare any activity that occurs within the network against its normal state, identifying any non-normal circumstances for further evaluation. This can include everything from sign-in attempts to network traffic volume, to the types of apps or devices being used.
It is worth noting that, because of the risk of AI systems incorporating sensitive data into output that could be accessed by outside users, an organization's specific data is not shared with or used by AI systems at other organizations. Instead, AI leverages threat intelligence gathered from multiple sources globally. This collective intelligence helps the AI systems identify and mitigate threats while maintaining confidentiality and security for all users.
It is worth noting that, because of the risk of AI systems incorporating sensitive data into output that could be accessed by outside users, an organization's specific data is not shared with or used by AI systems at other organizations. Instead, AI leverages threat intelligence gathered from multiple sources globally. This collective intelligence helps the AI systems identify and mitigate threats while maintaining confidentiality and security for all users.
When it comes to discovering potential threats, the devil really is in the details. By digging through gigabytes, terabytes, or even petabytes of data, AI systems can effectively recognize the subtle anomalous behaviors that are the fingerprints of system incursions. It does this through:
Machine learning
AI's machine learning algorithms can learn and improve as they encounter more data and connect it to various types of suspicious activities (such as unusual login attempts, abnormal file transfers, unexpected traffic, etc.). In essence, AI for security becomes smarter over time, allowing it to keep pace with threat actors.
Real-time monitoring
AI provides continuous, real-time monitoring of network traffic and user activities. This constant visibility is crucial, allowing organizations to respond to threats as they occur. Real-time monitoring ensures that even the slightest anomalies are detected promptly, reducing the risk of severe damage that can occur when a threat is allowed to remain within the network.
Threat intelligence integration
AI systems can integrate threat intelligence from multiple sources, providing a truly comprehensive view of emerging threats. By correlating data from various feeds, AI can identify new attack vectors and better anticipate potential threats.
Machine learning
AI's machine learning algorithms can learn and improve as they encounter more data and connect it to various types of suspicious activities (such as unusual login attempts, abnormal file transfers, unexpected traffic, etc.). In essence, AI for security becomes smarter over time, allowing it to keep pace with threat actors.
Real-time monitoring
AI provides continuous, real-time monitoring of network traffic and user activities. This constant visibility is crucial, allowing organizations to respond to threats as they occur. Real-time monitoring ensures that even the slightest anomalies are detected promptly, reducing the risk of severe damage that can occur when a threat is allowed to remain within the network.
Threat intelligence integration
AI systems can integrate threat intelligence from multiple sources, providing a truly comprehensive view of emerging threats. By correlating data from various feeds, AI can identify new attack vectors and better anticipate potential threats.
Detecting threats is only one aspect of cybersecurity; how effectively an organization responds to an attack can be the difference between a relatively minor disruption and a catastrophic breach that compromises sensitive data and inflicts lasting reputational damage. AI for security addresses this need through:
Automated response
AI can automate many aspects of incident response, such as isolating affected systems, blocking malicious activities, and notifying security teams. Automation reduces response times, limits the spread of threats, and ensures swift containment and mitigation—even when threat-response teams are unavailable.
Incident analysis
AI can assist in the analysis of security incidents by rapidly processing and correlating data from multiple sources. This capability helps human responders in better understanding the nature and scope of the threat, informing swift decision-making during response efforts.
Optimized collaboration
AI can facilitate better collaboration among security teams by providing detailed incident reports and actionable insights. This collaborative approach ensures that all stakeholders are working from a single, reliable source of truth so they can coordinate, align and guarantee the most effective response.
Rapid response for critical OT systems
In operational technology environments, where unexpected downtime can have severe consequences, reduced incident response times can make a major positive difference. The best OT rapid response capabilities apply advanced agentic AI workflows to support service teams, allowing them to more quickly assess threats, prioritize incidents, contain attacks on industrial systems, and accelerate decision making.
Automated response
AI can automate many aspects of incident response, such as isolating affected systems, blocking malicious activities, and notifying security teams. Automation reduces response times, limits the spread of threats, and ensures swift containment and mitigation—even when threat-response teams are unavailable.
Incident analysis
AI can assist in the analysis of security incidents by rapidly processing and correlating data from multiple sources. This capability helps human responders in better understanding the nature and scope of the threat, informing swift decision-making during response efforts.
Optimized collaboration
AI can facilitate better collaboration among security teams by providing detailed incident reports and actionable insights. This collaborative approach ensures that all stakeholders are working from a single, reliable source of truth so they can coordinate, align and guarantee the most effective response.
Rapid response for critical OT systems
In operational technology environments, where unexpected downtime can have severe consequences, reduced incident response times can make a major positive difference. The best OT rapid response capabilities apply advanced agentic AI workflows to support service teams, allowing them to more quickly assess threats, prioritize incidents, contain attacks on industrial systems, and accelerate decision making.
If recent advances have shown us anything it’s that AI is extremely versatile. This versatility allows it to be applied across the full breadth of cybersecurity, making it a powerful defensive tool for the digital era. The following are among the most noteworthy applications of AI for security:
Endpoint protection
AI enhances endpoint security by monitoring devices for suspicious behavior. Machine learning models can detect malware, ransomware, and other threats by analyzing file attributes, user activities, and system changes. This approach reduces the attack surface of an organization, limiting the attack vectors a threat actor may use to enter the network.
Vulnerability management
AI aids in scanning and assessing systems for vulnerabilities, prioritizing repairs based on potential impact, and recommending any necessary remediation actions. This process helps organizations address critical vulnerabilities promptly, before they can be exploited.
Behavioral analysis
By understanding normal user behavior, AI can identify deviations that may indicate malicious intent or compromised accounts. This capability is essential for detecting threats that traditional security measures might miss.
Cloud security
For networks that operate entirely or partially in the cloud, AI for security offers an easy solution for ensuring risk visibility. AI can identify vulnerabilities within cloud computing solutions, ensuring data integrity regardless of how distributed an organization's IT resources may be.
Endpoint protection
AI enhances endpoint security by monitoring devices for suspicious behavior. Machine learning models can detect malware, ransomware, and other threats by analyzing file attributes, user activities, and system changes. This approach reduces the attack surface of an organization, limiting the attack vectors a threat actor may use to enter the network.
Vulnerability management
AI aids in scanning and assessing systems for vulnerabilities, prioritizing repairs based on potential impact, and recommending any necessary remediation actions. This process helps organizations address critical vulnerabilities promptly, before they can be exploited.
Behavioral analysis
By understanding normal user behavior, AI can identify deviations that may indicate malicious intent or compromised accounts. This capability is essential for detecting threats that traditional security measures might miss.
Cloud security
For networks that operate entirely or partially in the cloud, AI for security offers an easy solution for ensuring risk visibility. AI can identify vulnerabilities within cloud computing solutions, ensuring data integrity regardless of how distributed an organization's IT resources may be.
The applications and capabilities described above are made possible through advanced AI tools. Some of the most significant AI tools used in cybersecurity include:
- AI-driven network intrusion detection and prevention systems
These systems are critical for identifying and stopping unauthorized access attempts in real-time, providing a frontline defense against cyberattacks. - Next-generation firewalls
Unlike traditional firewalls that rely on predefined rules (and are thus only effective against known threats), next-generation firewalls use AI to analyze traffic patterns and detect novel attacks. - XDR and SIEM
Extended detection and response (XDR) and security information and event management (SIEM) solutions use AI to aggregate and analyze data from various security products and log files. They provide security teams with a unified view of their security posture, helping to identify and respond to threats more effectively. - AI for Internet of Things (IoT) security
As IoT devices proliferate, securing these often-vulnerable endpoints is increasingly important. AI can detect anomalies and mitigate risks associated with IoT devices. - AI-enhanced OT security tools
AI agents can then enact threat-containment measures to prevent disruptions—all more quickly and with greater accuracy than is typically possible with manual monitoring.
AI security represents the next evolution in cyber defense, but is it essential? This can be a difficult question to answer definitively now. What is apparent is that ongoing advances in malicious programming and other threats are making traditional cybersecurity a much more difficult prospect. In fact, today approximately 60% of organizations believe that they would be unable to identify critical threats without artificial intelligence technologies (source: Capgemini). Consider the following benefits of AI for security:
AI helps reinforce gaps in the cybersecurity workforce
The pace of digital transformation is accelerating across the globe. Unfortunately, the availability of trained, experienced cybersecurity professionals is not keeping pace. Artificial intelligence can fill a talent gap that sees millions of unfilled cybersecurity jobs. It is also a scalable solution to use AI for security tools, as they augment the workflows of employees. At the same time, AI frees up valuable resources by reducing time to identify and triage threats. This allows workers to focus on more complex tasks, leaving simpler, more repetitive chores to automation.
AI promotes faster threat identification
Hunting down threats takes a great deal of time—single alert investigations can take days to complete. AI powered security tools can triage events, cutting down on time that is needed for incident response.
AI allows businesses to counter threat escalation
While some organizations have been slow to adopt the newest advancements in digital security, it’s clear that threat actors are showing no such hesitancy. Microsoft reports that “threat actors have rapidly increased in sophistication over the past year, using techniques that make them harder to spot and that threaten even the savviest targets” (source: Microsoft). AI for security empowers organizations with the technology they need to outpace this trend.
AI provides consistent protection
AI systems do not sleep, they do not take holidays, and they never let their attention wander; they remain fully capable and focused on all times. This creates a cybersecurity environment defined by constant vigilance, where attackers are unable to exploit schedule-related vulnerabilities or take advantage of human error.
AI is vital in automating threat analysis
Incident triage and analysis are necessities during the response process to eliminate false positives and determine how to contain and remediate incidents. Many companies use threat intelligence feeds as a part of their response process, and correlating the information automatically while leveraging enrichment from other tools can reduce the time spent on analysis. AI improves these processes by processing orders of magnitude more data in less time, and increasing the types of contexts, predictions, and historical insights that can be included.
The pace of digital transformation is accelerating across the globe. Unfortunately, the availability of trained, experienced cybersecurity professionals is not keeping pace. Artificial intelligence can fill a talent gap that sees millions of unfilled cybersecurity jobs. It is also a scalable solution to use AI for security tools, as they augment the workflows of employees. At the same time, AI frees up valuable resources by reducing time to identify and triage threats. This allows workers to focus on more complex tasks, leaving simpler, more repetitive chores to automation.
Hunting down threats takes a great deal of time—single alert investigations can take days to complete. AI powered security tools can triage events, cutting down on time that is needed for incident response.
While some organizations have been slow to adopt the newest advancements in digital security, it’s clear that threat actors are showing no such hesitancy. Microsoft reports that “threat actors have rapidly increased in sophistication over the past year, using techniques that make them harder to spot and that threaten even the savviest targets” (source: Microsoft). AI for security empowers organizations with the technology they need to outpace this trend.
AI systems do not sleep, they do not take holidays, and they never let their attention wander; they remain fully capable and focused on all times. This creates a cybersecurity environment defined by constant vigilance, where attackers are unable to exploit schedule-related vulnerabilities or take advantage of human error.
Incident triage and analysis are necessities during the response process to eliminate false positives and determine how to contain and remediate incidents. Many companies use threat intelligence feeds as a part of their response process, and correlating the information automatically while leveraging enrichment from other tools can reduce the time spent on analysis. AI improves these processes by processing orders of magnitude more data in less time, and increasing the types of contexts, predictions, and historical insights that can be included.
AI is more than some nebulous, blanket solution to improve security stances across industries; it is an essential solution that improves effectiveness within teams and departments. This includes the following:
Threat detection and prediction
Threat actors remain viable by constantly improving and refining their tactics, techniques, and procedures. AI provides cybersecurity systems with up-to-date and relevant knowledge of industry-specific and global threats, which help teams make critical decisions about priorities based on what attack strategies may be used against a company.
Incident response
AI systems provide context for response and prioritization to security alerts, fast incident responses, and root-cause analysis to mitigate vulnerabilities while analyzing how to prevent a recurrence of such issues.
Breach risk prediction
AI systems predict how an organization is likely to be breached. This allows the organization to better plan for resource allocation, mitigating weaknesses in the process. Prescriptive insights taken from AI analysis help improve configuration and enhancement controls to more optimize cyber resilience.
Controls effectiveness
Organizations should understand the various security processes and tools they have employed, and the subsequent impact of those processes and tools on their security posture. AI can improve evaluation of control-monitoring data, along with analyzing strengths and gaps in tool and process coverage.
Threat actors remain viable by constantly improving and refining their tactics, techniques, and procedures. AI provides cybersecurity systems with up-to-date and relevant knowledge of industry-specific and global threats, which help teams make critical decisions about priorities based on what attack strategies may be used against a company.
AI systems provide context for response and prioritization to security alerts, fast incident responses, and root-cause analysis to mitigate vulnerabilities while analyzing how to prevent a recurrence of such issues.
AI systems predict how an organization is likely to be breached. This allows the organization to better plan for resource allocation, mitigating weaknesses in the process. Prescriptive insights taken from AI analysis help improve configuration and enhancement controls to more optimize cyber resilience.
Organizations should understand the various security processes and tools they have employed, and the subsequent impact of those processes and tools on their security posture. AI can improve evaluation of control-monitoring data, along with analyzing strengths and gaps in tool and process coverage.
AI offers unmatched capabilities in terms of digital security. That said, its effectiveness depends on how it is deployed. Consider the following best practices:
- Evaluate AI capabilities
Businesses should assess the capabilities and limitations of AI security solutions before implementation. This includes understanding the specific needs of the organization and selecting AI tools that align with those security objectives. - Prioritize compliance and privacy considerations
Balancing the benefits of AI with data privacy regulations is essential. Organizations must implement AI systems that comply with relevant laws and protect sensitive data throughout the AI lifecycle. Otherwise, the organization may be liable should any sensitive data be exposed because of their AI solution. - Integrate with existing systems
Updating an organization’s approach to security does not need to mean starting from scratch; seamless integration of AI with current security infrastructure is vital for maximizing effectiveness. Businesses should ensure that AI tools can easily integrate with existing systems, enabling a cohesive and efficient security strategy.
AI makes sense to embrace in support of other products for key use cases. As the models mature and users become more confident in the technology (just as with automation), one can start to knit together individual tasks in an orchestrated sequence.
Modern security solutions are most effective with specific use cases, such as in uncovering and neutralizing phishing, spam, or opportunistic malware on endpoints with a high degree of confidence. As it does so, AI must be able to learn from these encounters, gathering observations and applying logical conclusions to improve its capabilities over time.
Core AI capabilities include the following:
Learning
Artificial intelligence is trained by feeding it large numbers of data artifacts, both structured and unstructured. AI improves its knowledge to understand cybersecurity and risks through machine learning and deep learning techniques.
Reasoning
Artificial intelligence gains insights, then reasons to identify relationships between different attributes. For example, AI might connect the dots between malicious files, insiders, or IP addresses and enrich findings with behavioral and historical insights. These analyses allow for exponentially quicker decisions as the AI gains more experiences from which to draw insights.
Automation
Organizations have the potential to optimize their automation efforts by combining them with AI advances. Automated processes collect vital data, and then AI runs the models. The result is improved analytical insights.
Modern security solutions are most effective with specific use cases, such as in uncovering and neutralizing phishing, spam, or opportunistic malware on endpoints with a high degree of confidence. As it does so, AI must be able to learn from these encounters, gathering observations and applying logical conclusions to improve its capabilities over time.
Core AI capabilities include the following:
Artificial intelligence is trained by feeding it large numbers of data artifacts, both structured and unstructured. AI improves its knowledge to understand cybersecurity and risks through machine learning and deep learning techniques.
Artificial intelligence gains insights, then reasons to identify relationships between different attributes. For example, AI might connect the dots between malicious files, insiders, or IP addresses and enrich findings with behavioral and historical insights. These analyses allow for exponentially quicker decisions as the AI gains more experiences from which to draw insights.
Organizations have the potential to optimize their automation efforts by combining them with AI advances. Automated processes collect vital data, and then AI runs the models. The result is improved analytical insights.
AI is changing cybersecurity operations, introducing intelligent, autonomous systems built to match modern attacks. And one of the most transformative developments in this field is the introduction of autonomous agents—AI-driven systems capable of independently detecting threats, initiating responses, and coordinating remediation efforts. These agents work continuously across digital environments, using real-time data to identify anomalies, assess risk, and act informed by predefined logic and dynamic learning models.
By applying advanced reasoning and automation, AI-powered agents can isolate compromised endpoints, trigger alerts, initiate containment protocols, and even assist in patch management—typically faster than human teams can react. This reduces the likelihood and severity of damage and gives security teams a major advantage. In operational technology (OT) environments, AI agents can accelerate incident response by correlating data across sources, identifying threats early, and suggesting immediate, context-aware actions to protect critical systems.
Generative AI (GenAI) further strengthens this approach by transforming how teams interact with complex security data. GenAI tools can summarize incidents, generate clear resolution notes, and even provide conversational interfaces that make it easier for users to understand and act on threat intelligence. But while GenAI enhances usability and insight, it’s the broader application of AI that is setting the pace for the future of proactive, responsive cybersecurity.
By applying advanced reasoning and automation, AI-powered agents can isolate compromised endpoints, trigger alerts, initiate containment protocols, and even assist in patch management—typically faster than human teams can react. This reduces the likelihood and severity of damage and gives security teams a major advantage. In operational technology (OT) environments, AI agents can accelerate incident response by correlating data across sources, identifying threats early, and suggesting immediate, context-aware actions to protect critical systems.
Generative AI (GenAI) further strengthens this approach by transforming how teams interact with complex security data. GenAI tools can summarize incidents, generate clear resolution notes, and even provide conversational interfaces that make it easier for users to understand and act on threat intelligence. But while GenAI enhances usability and insight, it’s the broader application of AI that is setting the pace for the future of proactive, responsive cybersecurity.
In only the past few years, the role of AI has expanded exponentially. In the next decade, that expansion is likely to accelerate, further enhancing AI's ability to detect threats with fewer false positives while enabling security operations teams to automate ever more complex tasks. This will allow AI to handle a broader range of cyberattacks, including specialized attacks targeting operational technology environments, and assist organizations in addressing vulnerabilities to improve their overall security posture. Throughout this transformation, security professionals will remain in high demand, but their overall responsibilities will need to evolve.
Tomorrow's cybersecurity teams will work alongside AI, allowing intelligent systems to handle everyday activities and even help coordinate incident response, while human experts will be able to focus the bulk of their time on strategic roles—not only responding to attacks, but going on the offensive to hunt down cyberthreats.
Unfortunately, advancements in AI will provide cyberattackers with increased capabilities. These threat actors will likely begin to invest more heavily in the use of intelligent systems to crack large volumes of passwords, create increasingly sophisticated phishing campaigns, and develop harder-to-detect malware. Attackers are more likely to target OT systems exploiting legacy systems and under-protected industrial networks. This escalation underscores the need for ongoing investment in AI by the security community; without which, organizations may not be able to keep pace with evolving threats. The future will see AI playing a crucial role in both defending against and conducting cyberattacks, making it a central element in the cybersecurity landscape.
Tomorrow's cybersecurity teams will work alongside AI, allowing intelligent systems to handle everyday activities and even help coordinate incident response, while human experts will be able to focus the bulk of their time on strategic roles—not only responding to attacks, but going on the offensive to hunt down cyberthreats.
Unfortunately, advancements in AI will provide cyberattackers with increased capabilities. These threat actors will likely begin to invest more heavily in the use of intelligent systems to crack large volumes of passwords, create increasingly sophisticated phishing campaigns, and develop harder-to-detect malware. Attackers are more likely to target OT systems exploiting legacy systems and under-protected industrial networks. This escalation underscores the need for ongoing investment in AI by the security community; without which, organizations may not be able to keep pace with evolving threats. The future will see AI playing a crucial role in both defending against and conducting cyberattacks, making it a central element in the cybersecurity landscape.
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The evolving role of AI in security is reshaping how organizations protect themselves against increasingly sophisticated cyber threats. ServiceNow delivers the advantages of AI-driven cybersecurity to organizations across essentially every industry, with the advanced AI capabilities of the ServiceNow AI Platform®.
ServiceNow Security Operations (SecOps) takes things even further with AI Agents for Security Incident Response. This solution summarizes security incidents, generates resolution notes, speeds up investigations, uses a wrap-up agent to close out security incidents, and provides analysts with quick correlation insights. With automated incident response, advanced threat intelligence, and predictive analytics—all operating out of a single, centralized platform—companies can operate securely in the knowledge that their proprietary data and digital networks are under the most intelligent protection available.
Finally, to gain full visibility into OT assets, automate OT security responses, and quickly mitigate vulnerabilities before they are targeted, ServiceNow delivers Operational Technology Management (OTM). ServiceNow integrates OT protection with broader enterprise security workflows, protecting IT and OT from evolving threats.
For businesses looking to stay ahead in the cybersecurity landscape, ServiceNow SecOps offers a comprehensive solution. Experience the future of AI-enhanced security; schedule a demo of ServiceNow SecOps today and discover how AI can transform your security operations.
ServiceNow Security Operations (SecOps) takes things even further with AI Agents for Security Incident Response. This solution summarizes security incidents, generates resolution notes, speeds up investigations, uses a wrap-up agent to close out security incidents, and provides analysts with quick correlation insights. With automated incident response, advanced threat intelligence, and predictive analytics—all operating out of a single, centralized platform—companies can operate securely in the knowledge that their proprietary data and digital networks are under the most intelligent protection available.
Finally, to gain full visibility into OT assets, automate OT security responses, and quickly mitigate vulnerabilities before they are targeted, ServiceNow delivers Operational Technology Management (OTM). ServiceNow integrates OT protection with broader enterprise security workflows, protecting IT and OT from evolving threats.
For businesses looking to stay ahead in the cybersecurity landscape, ServiceNow SecOps offers a comprehensive solution. Experience the future of AI-enhanced security; schedule a demo of ServiceNow SecOps today and discover how AI can transform your security operations.
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