The advantages and disadvantages of AI in cybersecurity

5 advantages of AI in cybersecurity

AI has radically changed how cybersecurity practitioners detect and respond to emerging threats. AI aids in cybersecurity areas such as threat detection, response time, predictive analytics, alerts and patch management.

1. Improved real-time threat detection

AI security tools can greatly improve threat detection by analyzing large volumes of network traffic from various sources, including user behavior and system logs. For example, AI tools can identify abnormal patterns in network traffic, which could indicate an advanced persistent threat that traditional security tools might overlook.

AI tools also excel at detecting and categorizing patterns. Machine learning models trained on massive data sets can identify and categorize malicious patterns efficiently based on historical data. This can help pinpoint threats and enhance network security.

2. Improved response time

Automating incident investigation streamlines the detection, analysis and containment of security threats.

When an incident occurs, AI systems can immediately correlate real-time data across various security platforms, such as firewalls, endpoint protection systems, intrusion prevention and intrusion detection systems (IDSes), and security information and event management tools. These integrations enable AI to quickly reconstruct the attack timeline.

For example, in a ransomware attack, an AI tool could identify the root cause and affected systems, prioritize risks, and recommend containment strategies -- a process that might require hours or even days of manual effort. With AI, however, the process can start within minutes, significantly minimizing potential damage.

3. Predictive security analytics

By analyzing historical security incidents, AI models can forecast potential vulnerabilities before threat actors exploit them. For example, a financial institution's AI tool can recognize unusual authentication patterns as a potential credential-stuffing attack based on similar patterns detected in previous breaches.

4. Reduced false positive alerts

AI can combat alert fatigue by reducing false positive notifications. For example, large financial institutions process many daily transactions. Traditional security tools might flag high volumes of login attempts as suspicious, triggering hundreds of daily alerts for human analysts to inspect manually.

An AI-driven system, on the other hand, can analyze historical transaction data, user behavior and other contextual information to identify normal operations patterns. This enables security teams to focus on real threats rather than spending hours investigating benign anomalies.

5. Automated patch management

AI's automation capabilities can assist with routine security tasks.

For example, AI can automate vulnerability assessments across thousands of systems in an IT environment. This improves the security team's ability to make informed decisions, such as adjusting access controls based on risk scores and continuously monitoring security configurations for gaps. When a critical vulnerability is discovered, AI systems can identify affected assets, prioritize patches and verify successful remediation.

5 disadvantages of AI in cybersecurity

While AI security tools offer important advantages in cybersecurity, they also pose several disadvantages.

1. High implementation costs

Using AI in cybersecurity requires an initial investment, especially for organizations with both on-premises and cloud IT environments. Deploying AI across hybrid environments can require specialized hardware like GPUs for model training, enterprise-grade software licenses and skilled personnel such as data scientists and security architects.

2. Maintenance complexity

AI security tools require continuous model refinement, regular data updates and ongoing performance optimization. Organizations must maintain dedicated teams for model retraining, feature engineering and addressing issues. Small organizations often struggle to meet these requirements if they lack the technical expertise and budget to maintain sophisticated AI security tools effectively.

3. Adversarial attacks

AI tools face unique attacks targeting their underlying machine learning infrastructure. Common attack vectors include the following:

• Data poisoning attacks. In a data poisoning attack, adversaries manipulate training data by injecting carefully crafted malicious samples to force the AI tool to misclassify future attacks.
• Extraction attacks. Adversaries reverse-engineer model parameters and decision boundaries to reveal model architecture and training data. The data revealed might contain proprietary source code or sensitive information.
• Evasion attacks. Adversaries modify attack patterns to exploit models' blind spots, such as slightly modifying malware signatures to prevent the system from detecting certain types of malware while maintaining overall functionality.

4. Privacy implications

Some AI security tools process vast amounts of sensitive data. Healthcare organizations implementing AI-based threat detection, for example, must ensure HIPAA compliance when processing patient or other sensitive medical records. Likewise, financial institutions must maintain PCI DSS standards while analyzing customers' transaction patterns.

5. Ethical considerations

AI in cybersecurity can raise complex ethical issues. For example, behavior-based anomaly detection might erroneously flag certain individuals or groups. In addition, accountability can be difficult to determine when an AI tool makes an incorrect security decision.

6 best practices for using AI in cybersecurity

AI-powered cybersecurity tools can enhance security and help organizations mitigate threats. Adopt these best practices to manage risk.

1. Ensure human oversight

Maintain human oversight over any critical decisions. Although AI can detect and flag abnormal behavior, some decisions -- such as blocking access to sensitive resources or escalating incidents -- should always involve human review to prevent false positives and ethical issues.

2. Establish a clear AI security policy

An AI security policy ensures fair use of AI, defines accountability boundaries and addresses challenges related to automated decision-making. Organizations must clearly understand the risks and limitations of letting AI operate without human intervention. For example, organizations operating in highly regulated industries such as healthcare should specify where AI systems can -- and can't -- operate, such as when handling sensitive patient data.

3. Use high-quality training data sets

Train models with unbiased, high-quality data sets to minimize inaccuracies. To enhance precision and protect user privacy, tailor training data to the AI tool's specific functions. For instance, models designed to detect unauthorized login attempts should be trained on logs of failed attempts, while avoiding unnecessary sensitive customer information.

4. Combine AI with traditional security tools

Combine AI tools with traditional security tools like firewalls, intrusion detection systems and manual audits to build a multilayered defense strategy. This approach provides comprehensive protection against an array of cyberthreats.

For example, a retail company can integrate an AI-driven endpoint protection system with its existing IDS. While the AI system identifies advanced malware based on behavior, the IDS focuses on signature-based detection of known threats. This layered approach protects against both emerging and previously executed attack techniques.

5. Keep models up to date

Regularly update AI tools and underlying models to maintain their effectiveness against evolving threats. Continuous updates ensure the system can detect and respond to the latest attack vectors. For example, if an organization uses an AI-powered phishing detector, it should regularly update the tool with the latest attack techniques to prevent emerging phishing attacks.

6. Implement access controls

Secure AI system access with strong authentication mechanisms, such as multifactor authentication (MFA). This ensures that only authorized personnel can modify AI configurations or access sensitive data, reducing the risk of insider threats and unauthorized changes.

For example, a tech company deploying an AI-powered threat analysis tool might use MFA to protect access to the system dashboard. Only those with MFA access can modify configurations or review sensitive analytics. This ensures unauthorized users cannot tamper with critical settings for malicious purposes.

Nihad A. Hassan is an independent cybersecurity consultant, expert in digital forensics and cyber open source intelligence, blogger, and book author. Hassan has been actively researching various areas of information security for more than 15 years and has developed numerous cybersecurity education courses and technical guides.