Modern cybersecurity depends on two factors: spotting real threats and neutralizing them before they can damage the business. In theory, this sounds simple, but putting those factors into practice is another matter entirely.
Increasingly complex infrastructures, dependencies and access demands are creating new and nuanced attack vectors that can be breached. Software releases, patches and updates are regularly followed by zero-day attacks. Human error and social exploitation from malware-infected downloads, questionable websites and phishing attacks are a constant worry. Even new hardware -- such as servers and IoT devices -- ships with potential vulnerabilities already baked in. At the same time, potential consequences to businesses are greater than ever, with increasingly diverse compliance and legal issues to address.
Traditional security methods are often just inadequate to meet the latest evolving threats.
Modern artificial intelligence technologies are quickly emerging to provide speedy threat detection, accurate threat determination, immediate response to effectively counter the threat and real-time adaptation to changing threats. AI can also be proactive, analyzing vulnerabilities and activities to predict -- and prevent -- potential attacks. And every threat neutralized by a cybersecurity infrastructure is a vital cost savings for the business.
What is AI-powered threat detection?
AI-powered threat detection involves the creation, training, deployment and management of cybersecurity systems to accelerate accurate threat detection and mitigation. Such systems use machine learning (ML) to analyze large volumes of activity data across the enterprise. Activity data involved in an ML algorithm analysis can include the following:
- Network traffic patterns and packet payloads.
- Application or other configuration effects.
- Data access and content effects.
- User behaviors.
The key to AI-powered threat detection is in the analytical prowess of machine learning. In effect, AI-powered threat detection learns the normal -- or allowed -- behaviors of the environment, understands an array of existing threats and looks for deviations or anomalies from the historical baseline. It's these differences or exceptions -- sometimes too subtle for traditional security tools to detect -- that can signal possible malicious activity.
Once a machine learning algorithm indicates a potential threat, the AI layer of the cybersecurity platform can take automatic and autonomous action. AI responses can include the following:
- Denying access to data or applications.
- Prohibiting unauthorized changes to data or applications.
- Stopping network traffic or user access.
- Creating detailed anomaly logs.
- Alerting security teams for further investigation.
AI-powered threat detection can also evolve and refine its decision-making over time. It can learn from historical data -- regularly updating the activity baseline and adjusting alerts to meet changing normal activity levels. It can also learn from human feedback, allowing security teams to respond to AI-generated alerts and use human determinations to make further refinements to alerts and responses. For example, if X activity looked suspicious and a human expert determined an appropriate response to be Y, then adjust future responses to X activities accordingly.
Benefits of AI-powered threat detection
AI-powered threat detection offers many business benefits, including the following:
- Faster speed. ML is recognized for its ability to process and analyze huge volumes of information quickly. This allows for quick learning and rapid threat detection, which are critical for mitigating modern security threats. AI can also act quickly, issuing an appropriate response to perceived threats and alerting security teams for closer evaluation.
- More automation. AI-powered threat detection makes extensive use of automation capabilities, allowing the security platform to act with speed and autonomy. AI platforms can handle threat detection as well as vulnerability analysis, patch management and incident response. This frees human security staff to monitor the environment, focus on actual incidents and consider more strategic activities rather than constant alert "firefighting."
- Better accuracy. The same accuracy and insights that ML brings to business analytics are well-suited to cybersecurity. AI-powered threat detection can see patterns and detect anomalies that traditional tools can miss. Moreover, AI can limit false positives -- providing greater confidence in threat presence and response.
- Proactive threat management. The analytics and insights provided by ML analytics can identify potential vulnerabilities and possible attack vectors before an attack occurs. It can even predict possible attacks. This allows security professionals to prevent threats and enhance security postures on a proactive -- rather than reactive -- basis.
- Adaptive behavior. AI-powered threat detection platforms can learn from analyzed data, changing conditions and human security responses. This allows the ML models and AI responses to constantly improve over time. It can also adapt to the unique risk tolerance, security needs and response requirements of the individual business.
- Consistent responses. AI-powered threat detection reduces dependence on human judgment and responses. This can reduce the significant impacts of human error and ensure more predictable and consistent responses to threats. This can benefit business continuity and regulatory compliance postures.
How AI is used for threat detection in the enterprise
AI has demonstrated extraordinary capabilities in data analytics and adaptable workflow automations. These capabilities are being embraced by AI designers and are already finding traction in various AI-powered cybersecurity tools, including the following:
- Attack simulation. Generative AI can formulate and launch simulated attacks on an organization. This allows cybersecurity experts to test existing defenses, find and validate potential vulnerabilities, and enhance threat detection models by stress-testing and further training AI-powered threat detection systems.
- Network security. Network detection and response systems use AI to monitor network traffic, analyze traffic sources and patterns, examine network packet payloads, and identify complex and subtle threats that can circumvent traditional network security tools.
- Endpoint security. Endpoint detection and response (EDR) systems use AI to manage endpoint devices such as laptops, desktops, and other devices. EDR systems can analyze device activity and user behavioral patterns to detect and respond to potential threats or malicious activity.
- Infrastructure security. Security information and event management (SIEM) systems use AI to analyze security logs from hardware and applications. By learning normal behaviors and understanding common exceptions, a SIEM platform can quickly analyze and identify potential threats occurring across the enterprise infrastructure.
- Physical security. Physical threats -- such as device tampering or theft -- are often overlooked as cybersecurity threats. AI-powered image and video analysis can recognize faces or other biometrics, authenticate roles or access based on biometrics and alert security staff if someone behaves inappropriately.
How to implement AI threat detection systems
Every business and its needs are different, so there is no single methodology to implement an AI-powered threat detection system into an enterprise security infrastructure. Proper implementation requires strategic planning, technical knowledge and constant refinement. However, there are some important guidelines that can help to improve the outcome of an implementation, including the following:
- Start with an end in mind. Any project requires a goal. Identify the types of threats that the business must address with the AI system, the goals intended for the AI system -- such as automating threat identification and mitigation -- and establish an appropriate scope for the AI system.
- Define success. Consider the criteria that define a successful AI system implementation. This may involve a selection of relevant metrics -- such as threats detected, threats mitigated or even a ratio of the two. Metrics can typically be configured and displayed in a management dashboard for the AI system. Any deviation from the successful criteria can provide a justification for further investigation and system refinement post-deployment.
- Select the AI system. A suitable AI-powered threat detection system must be built or chosen -- often after careful comparison, evaluation and proof-of-concept (PoC) trials. AI systems can be selected based on detection capabilities such as anomaly detection, pattern recognition or behavioral analysis. Systems can also be selected to integrate well with the existing security infrastructure or used in concert with other traditional security tools.
- Organize and prepare training data. An AI system will need to be trained, so it's important to identify, collect and prepare required data -- including system, network and user activity logs. As with most AI training, data will need to be cleaned, normalized and transformed to create uniform formatting and content. Be sure to observe all data protection and privacy guidelines while accessing and preparing training data.
- Train and validate the AI. Use the prepared training data to train the AI system's ML models. This may take some time and effort. Validate the trained model by checking its accuracy and performance. Monitor ongoing performance and update the training periodically as new threats or baselines demand.
- Deploy the AI. Once trained and validated, the AI system can be deployed to production. This often requires some amount of integration with other security tools -- such as SIEM platforms or intrusion detection/prevention systems. Have a well-understood rollback plan. Take care to configure the AI carefully and develop suitable alerting and automation workflows to handle any threats the AI is trained to identify. This may require a period of testing or blue/green deployment to ensure the AI is operating as expected.
- Monitor and update the AI. Once deployed, the AI system should be monitored constantly to ensure its proper performance and recognize possible areas for improvement -- such as improving automation workflows or enhancing the accuracy of certain threat recognition. Any AI will require periodic updates to the model -- retaining and resetting new baselines as conditions and threats evolve over time.
- Train the security staff. AI-powered threat detection is intended to supplement -- not replace -- human security teams. Be sure to provide staff with comprehensive training for the AI tool and its use -- such as creating automation workflows and AI training procedures. AI systems should be explainable, and staff should clearly understand how the AI makes its decisions.
Challenges and limitations for AI threat detection systems
Despite the benefits and capabilities, AI-powered threat detection faces several challenges that business and technology leaders should consider carefully before implementing -- especially in mission-critical areas such as cybersecurity. Common challenges and limitations include the following:
- Data privacy. AI accesses, stores and analyzes enormous amounts of data. Such data is often sensitive to the business and can potentially include personally identifiable information about users. The ways in which this vast amount of data is stored, accessed, used and conveyed must adhere to prevailing regulatory obligations and legislative frameworks. Strong data protection and retention policies are required.
- Ethical use. Just like data privacy challenges, the data generated, accessed and used by AI-powered threat detection systems must be used only by authorized individuals for acceptable business purposes. Using security data and analytics for other purposes -- such as finding and exploiting a vulnerability in a business competitor -- must be prevented.
- Explainability. A persistent challenge with all AI is explainability -- the transparency needed to understand how an AI actually works and uses data to make decisions. Explainability builds trust and allows the business to demonstrate confidence in the AI platform. A lack of explainability erodes the trust of business leaders, employees, partners, users and other stakeholders.
- Bias. Machine learning algorithms can be powerful and effective, but they are only as good as the data used to train them. Bias in the training data can lead to inaccurate, unfair or discriminatory decisions made by the AI. Data scientists and developers responsible for building and training an AI system must carefully curate training data to eliminate potential bias, which can skew assessments.
- AI-as-attacker. While generative AI can be used to simulate attacks, malicious actors can also use AI tools to launch real attacks, finding and exploiting vulnerabilities. Some AI attack mechanisms can be used to fool an AI-powered threat detection system. Cybersecurity experts must be vigilant about the weaponized use of AI tools.
How to evaluate AI threat detection solutions
Beyond everyday issues such as cost, support and ease-of-use, chief information security officers (CISOs) and their teams should carefully evaluate key elements of an AI-powered threat detection system before adopting the technology. Common questions that a CISO might seek to answer include the following:
- What types of threats will the system detect? It's important to first consider which threats need to be detected and then consider tools capable of addressing those threats. Common threat types include malware, phishing, network intrusion, insider attacks, system and user behavioral analytics, anomaly detection and pattern recognition.
- What are the system's performance features? Determine the specific features and capabilities of the platform -- such as effective real-time threat detection and mitigation, training options and requirements, its ability to refine learning and adapt to new or changing threats, and scale to handle larger volumes of security or threat data.
- How accurate is the system? Even the best AI-powered threat detection systems are not perfect. Evaluate the platform's accuracy. This may require a PoC deployment to determine whether the platform will find and stop the types of threats required. Further, the platform should minimize false positives. Consider how often new algorithms are released or updated.
- Does the system integrate with the current security infrastructure? Consider how well the AI-powered threat detection system interoperates with existing infrastructure -- especially current security tools such as malware detection, firewalls, endpoint security, and SIEM tools. Avoid systems that will require fundamental changes to other elements of the security infrastructure.
- How much work can the system automate? A central part of AI-powered threat detection is workload reduction for security teams. Consider how many tasks the AI system can automate, including threat detection and autonomous real-time responses. An AI system that depends on human oversight is not fully using AI capabilities.
- How does the system communicate? Even the most capable and autonomous security system must communicate alerts, generate reports and provide appropriate context to security teams. Evaluate the ways that an AI-powered threat detection system generates, prioritizes and delivers alerts to analysts.
- Does the system maintain compliance? Consider the role of human oversight on the AI system and ensure that the system will support business continuity and regulatory compliance obligations.
Stephen J. Bigelow, senior technology editor at Informa TechTarget, has more than 30 years of technical writing experience in the PC and technology industry.