Artificial Intelligence (AI)

The technology used by cyber attackers to bypass your defenses is the same technology you need to stop them. This creates a problem for organizations that don't adopt AI-driven security, because they fall behind in capability and speed.

The World Economic Forum found that 47% of organizations cite generative AI as their primary security concern, and that 86% of business leaders experienced at least one AI-related incident in the past year. Even the FBI documented a 37% increase in AI-assisted business email compromise attacks. AI lowered the barrier for attackers, making sophisticated techniques available to anyone with basic technical skills.

Traditional solutions don’t work anymore. Data shows that 76% of malware now consists of zero-day threats that traditional antivirus doesn't recognize. That difference between detecting three-quarters of threats versus missing them explains why security teams are moving to AI-powered detection.

The Role Of Generative AI in Cybersecurity

Security teams use generative AI to identify threats faster, analyze malware code, and monitor network behavior, to combat GenAI cyber security threats. AI is used in cybersecurity in these areas:

• Improving threat detection and response: Organizations that integrate AI into their security operations to boost speed and volume of threat detection over traditional methods. To succeed in this environment, organizations must now use security AI and automation in their operations centers to handle alert triage and behavioral analysis. This must be done to analyze data patterns and flag anomalies that indicate potential attacks.
• Reverse engineering phishing and malware attacks: AI systems can analyze malicious files to understand how they operate, what vulnerabilities they exploit, and how they communicate with command-and-control servers. These systems may disassemble malware code, trace execution paths, and identify the techniques attackers use to evade detection. The technology can extract indicators of compromise from encrypted samples, allowing security teams to develop signatures and detection rules faster than manual analysis would permit. This automation can handle the volume of new malware variants that appear daily, identifying families of related threats and tracking how attackers modify their tools.
• Improving endpoint and network security: AI can monitor traffic patterns to identify anomalies that might indicate compromise or policy violations. These systems establish baselines of normal behavior for users, devices, and applications, then flag deviations that may warrant investigation. The technology operates at machine speed, analyzing thousands of events per second to detect threats that could overwhelm human analysts. AI can dynamically adjust access controls based on user behavior and context, restricting privileges when it detects suspicious activity or granting access when behavior matches established patterns.

AI Security Risks

Attackers use AI to automate attacks, create convincing social engineering, and find vulnerabilities faster than defenders can patch them. Some examples of such AI security risks are below.

• Adversaries and AI-powered bots: Automated bots now handle a significant portion of internet traffic, with malicious bots executing credential stuffing attacks, scraping data, and testing websites for vulnerabilities. These bots incorporate machine learning to adapt their behavior in real time, rotating IP addresses, mimicking human browsing patterns, and adjusting their tactics when they encounter security controls. AI tools lowered the technical barrier for launching bot attacks, allowing actors without deep programming knowledge to deploy sophisticated botnets that previously required expert skills to operate.
• Phishing, social engineering, and deepfakes: AI eliminated the grammatical errors and awkward phrasing that users relied on to identify phishing attempts. Language models generate convincing email content that matches corporate communication styles, making it harder to distinguish legitimate messages from attacks. Deepfake technology creates audio and video that impersonates executives, enabling attackers to authorize fraudulent wire transfers or trick employees into sharing credentials. These attacks succeed because they exploit trust relationships and bypass technical security controls by targeting human decision-making.
• Exploiting vulnerabilities: The window between vulnerability disclosure and exploitation has collapsed as AI automates the process of analyzing patches, reverse engineering vulnerabilities, and generating working exploits. Attackers use AI agents to scan networks for vulnerable systems, test exploit code, and move laterally through compromised environments without manual intervention. This automation compresses attack timelines from weeks to hours, giving defenders less time to patch systems before attackers weaponize newly discovered vulnerabilities.

Machine Learning Provides Predictive Analytics, Anomaly Detection, and More

Machine learning algorithms identify patterns in historical attack data to recognize the signatures of attack preparation, giving defenders time to block access or isolate compromised accounts before attackers execute their payload through the use of behavioral analysis and anomaly detection.

Security tools establish baselines of normal activity by observing how users access systems, what data they touch, and when they perform these actions. ML models detect deviations from these baselines because they operate on probabilities rather than fixed rules. You can apply behavioral analysis to catch threats that signature-based antivirus misses, contributing to an increase in detections.

The Rise of Automation in Security Operations

Security operations centers receive thousands of alerts daily, most of which are false positives or low-priority events. Automation handles the repetitive work that consumes analyst time by correlating alerts from multiple sources, enriching them with threat intelligence, and executing initial response actions without human intervention.

Examples of security automation tools:

1. XDR (Extended Detection and Response)

XDR platforms aggregate security data from endpoints, networks, email, and cloud services into a single view. Traditional security tools operate in isolation, forcing analysts to pivot between consoles and manually correlate events. XDR automates this correlation.

2. SOAR (Security Orchestration, Automation, and Response)

SOAR platforms execute predefined playbooks when specific conditions occur. These systems integrate with existing security tools through APIs, orchestrating responses that would otherwise require manual coordination between multiple products.

3. Vulnerability management

Automated vulnerability scanners continuously test networks and applications for known weaknesses. When a new CVE is published, these systems immediately scan the environment to identify affected systems and prioritize patching based on exploitability and exposure.

4. AIOps (Artificial Intelligence for IT Operations)

AIOps applies machine learning to IT operations data, identifying performance degradations or anomalies that indicate security issues. AIOps tools detect these anomalies in operational metrics that traditional security tools don't monitor.

Preserving the Human Element

Automation helps handle volume, but humans make judgment calls that machines cannot. Security professionals are needed to review the cases that automation cannot resolve, investigate sophisticated attacks that don't match known patterns, and make decisions about acceptable risk.

The work itself is changing. Analysts used to spend most of their time triaging alerts, determining which ones required investigation and which were false positives. Now they design the detection rules that generate those alerts, tune the ML models that filter noise, and build the response playbooks that automation executes. This shift means security professionals need to understand both the business threats they're defending against and the technical capabilities of their tools. They're becoming architects of security systems rather than operators who manually respond to every incident.

Ethical AI adoption in security

AI systems trained primarily on attacks against one industry might miss techniques common in another. A model that learned from financial services breaches won't necessarily catch healthcare-specific attack patterns. Security teams need to validate their AI tools across the actual environments and threats they face, not just trust vendor claims about accuracy. Automated response actions also carry risk—a false positive that locks out legitimate users during a business emergency creates its own kind of damage. Human oversight matters because someone needs to review automation decisions and maintain accountability for actions taken in the organization's name.