In his 2014 book “Zero to One,” Peter Thiel explained how technology drives exponential growth. AI has delivered on that promise, helping organizations scale operations and automate processes at unprecedented speed. But there’s a problem: cybercriminals are achieving the same exponential growth in their attacks. The same AI tools that help businesses scale revenue are now scaling phishing campaigns, malware variants, and data breaches at a pace that’s outstripping traditional defenses.

While organizations debate AI governance and weigh efficiency gains against privacy concerns, cybercriminals face no such hesitation. They’re already weaponizing large language models and automated vulnerability scanners to launch cyber threats that evolve faster than defenders can adapt.

Along this article, we’ll explore the role of AI in cybersecurity, from how it powers sophisticated phishing campaigns and deepfake attacks to how defenders can leverage it for real-time threat detection, behavioral analysis, and automated incident response.

How cybercriminals use AI in Cyberattacks?

Cybercriminals are weaponizing AI in multiple ways, making threats more undetectable, persistent, and malicious than ever before and organizations are struggling to keep-up. In a recent research carried out by Crowdstrike, it was found that 76% of organizations struggle to match the speed of AI powered attacks.

Here’s how cybercriminals are using artificial intelligence to orchestrate threats:

Malicious Tools

Gen AI-based tools like Fraud GPT and Worm GPT are now frequently sold in underground dark web forums. Cybercriminals use these tools to deceive, evade, and disrupt by creating unique strains of malware, more effective lures for phishing, and offering instructions to evade tight security measures.

In its recent study based on close monitoring of darkweb forums, threat intelligence firm Kela found that there was a whopping 52% surge in discussions related to jailbreaking GenAI based legitimate tools like ChatGPT.

It also found an increase in frequency of new malicious AI based tools that offer holistic threat arsenal to orchestrate attack regardless of the skill level of attackers.

Phishing

Cybercriminals are using AI to make their phishing campaigns more effective by hyper-personalizing them through the adjustment of the tone and style of communication. They improve their social engineering campaigns by using AI to analyze their targets’ conversations on social media, emails, and other public forums.

AI based malicious tools are enabling non-native English threat actors with beginner-level skills to create a sophisticated phishing campaign from scratch. There is a rise in “AI-as-a-service” in cybercrime forums on darkweb where cybercriminals can get access to subscription based malicious AI tools lowering. There is an entire ecosystem in darkweb dedicated to AI powered cybercrime. 

Deception

Deepfakes aren’t a thing of the future anymore. They are widely used by cybercriminals to pose threats like extorting ransom in exchange for defamation, fooling victims to make transactions by impersonating a senior-level executive, spreading misinformation, and snooping on people to extract confidential information.

Another way cybercriminals are using AI for deception is through steganography, where they use AI to improve the precision of embedding malicious payloads into image files that closely resemble the legit ones.

Reconnaissance

Attackers use AI to carry out reconnaissance campaigns in multiple ways. They perform passive reconnaissance by gathering information without directly interacting with the target system through publicly available databases and social media. They also conduct active reconnaissance through direct interaction with target systems via security configuration scanning and network probing.

AI automates critical reconnaissance activities including data collection from websites, social media, and metadata, Open Source Intelligence gathering from public databases, cloud platforms, and networks, vulnerability scanning, and password pattern analysis.

Attack Automation

Promptlock, a fully AI-powered ransomware, already exists. Even though it was a university experiment by a group of professors and students, it showcases how attackers can effectively orchestrate all stages of a ransomware attack, from initial reconnaissance to generating a ransom note.

It proves how dangerous cyber threats can become when paired with AI, with the ability to target multiple platforms, create malicious payloads, and automatically improve based on information gathering.

The AI-Powered Threat Landscape Defenders Face

Modern AI-driven cyber threats are specifically designed to bypass Endpoint Detection and Response (EDR) systems. Cybercriminals use AI to create malware that automatically exploits zero-day vulnerabilities and performs reconnaissance to identify sensitive folders for data exfiltration or destruction. They use LLM-based tools like Fraud GPT to create hyper-personalized phishing campaigns and malware with unique signatures that are difficult to detect and highly persistent, allowing attackers to maintain presence for months without alerting defenses.

Defenders face significant challenges detecting threats that mask their presence. Attackers can exploit vulnerabilities in hours, while organizations typically rely on month-long patching cycles. In 2024, there was a 61 percent surge in organizations using vulnerabilities with 30-day-old patches, according to Action1’s 2025 Software Vulnerability Report.

AI provides powerful defense capabilities for security teams. AI uses technologies like Artificial Neural Networks, deep learning, and big data analytics to spot suspicious patterns, behaviors, and activities, analyze large datasets, and detect and respond to threats.

How Organizations use AI for Cybersecurity and Defense?

Organizations use AI for cybersecurity defense by automating real-time threat detection and analyzing network behavior patterns. AI systems can predict attacks before they occur, detect suspicious user activities, and automatically respond to phishing attempts. They also streamline incident response through automated analysis and system isolation, significantly reducing response time.

Here are some ways through which it can help defenders:

Improve Their Detection Capabilities

AI can help cybersecurity teams automate and improve the precision and speed of threat detection by performing real-time analysis of large volumes of telemetry from multiple security solutions. AI algorithms can be trained based on threat intelligence from multiple reputable sources to identify known threats and track user behaviors, system activity, and network data for anomalies.

It can classify threats based on deviation in behavior, activity, or any alteration of data. AI-based threat detection can help organizations reduce the time difference between detection and response.

In one survey carried out by Darktrace, 95% of security teams in organizations agreed that AI helps improve speed and efficiency of prevention, detection, response, and recovery.

Predict Patterns

AI can perform rapid system analysis to identify and mitigate sophisticated threats through real-time recognition of patterns and signs that could pose a threat to the network and data.

It can use machine learning algorithms to swiftly detect the presence of malicious software based on unusual system activity and file modifications. It can predict threats before they occur by analyzing terabytes of data across a network to quickly detect indicators of compromise using advanced analytics.

By combining multiple insights and global threat intelligence data, it can help organizations evolve with threats instead of reacting to them. They can strategize by knowing where cyber threats will strike next and whether cybercriminals are orchestrating a reconnaissance or attack campaign through machine learning models that correlate data to identify emerging patterns.

Respond to Suspicious Behaviors and Activities

Modern-day attackers can move past traditional detectors that rely on patterns, signatures, and behaviors. AI solves this by analyzing user behaviors, creating a baseline based on insights like logs/usage patterns with context like login times, resource access patterns, data transfer volumes, etc., and responding & alerting security teams in case of deviations from the baseline.

For example, AI can quickly detect if a user who logs in every day from San Francisco, California, at 10 AM logs in from Tokyo, Japan, at 4 AM. It not only helps detect and prevent zero-day, known, and unknown threats, but also helps identify the signs of insider threats, detect compromised accounts, reduce the dwell time of attackers with early detection, and help investigators with a holistic context of user behaviors.

Improve IAM (Identity and Access Management)

With AI, organizations can implement zero trust with least privilege-based permission management and adaptive access control that moves beyond static roles and predefined rules. Users can be automatically flagged through the identification of anomalous behavior based on continuous analysis of their access patterns. 

It can offer holistic insights into security with a comprehensive assessment of network security posture and factors like user behavior, device health, and geolocation.

It can also enhance user experience with adaptive policies that prevent disruptions.

Detect and Respond to Social Engineering and Phishing Attacks

Security teams can use AI to prevent and rapidly respond to phishing and other social engineering attempts. AI models can be trained on existing data (like email datasets) on social engineering and phishing attacks for quick pattern recognition.

Using predictive analytics, it can automate response actions like flagging and quarantining suspicious communication.

It can enable organizations to proactively build their human firewall by bridging awareness gaps by using AI to design training programs through continuous analysis of user behaviors and assessment of their phishing simulation scores.

Free up Security Teams Using AI Agents

Security teams can automate routine low-risk security tasks like performing hygiene security checks or compliance checks. They can improve their focus on critical security alerts and address challenges like alert fatigue, tool fragmentation, and queue cluttering by using AI’s capability of rapidly analyzing vast datasets. It can also help organizations improve their Mean Time To Respond through automated responses based on planned protocols. 32% of organizations are extensively using AI and automation for security in 2025, according to IBM.

Incident Response

Incident response processes can be made much efficient with AI in multiple ways. IR actions can be automated using AI, like root cause analysis, triaging & categorization of incidents as per severity, isolation of compromised systems, blocking malicious attempts, and improving response playbooks based on past incidents.  

It can aggregate and normalize telemetry data (like logs and traffic) and intelligence feeds to identify and signal potential security risks. Through the extraction and translation of reports/information from non-English sources, it can help make sense of communication from global sources. 

Additionally, it can also assist security teams through summarized information of incidents, comprising their nature, severity, impact, and mitigation steps that must be followed.

Continuous improvement

AI enables continuous security improvement by learning from new data, past incidents, and response patterns. It can create accurate simulations of sophisticated modern threats that help security teams understand attack methods. These threat simulations allow security teams to refine their defenses based on comprehensive analysis of historical threat data and incident response outcomes.

Monitor Physical Security

Organizations can improve physical security through AI-based monitoring of premises. It can be used to analyze video surveillance feeds using visual intelligence to identify unauthorized access attempts.

Upon detection of unauthorized activity, it can record evidence and logs offering context for further investigation and action. It can also help investigators with quick analysis and correlation of massive data sets containing forensic evidence and other data, and aggregate the findings into a detailed report.

Conclusion

AI has transformed cybersecurity defense by enabling real-time threat detection, predictive analytics, and automated incident response at a scale impossible for human teams alone. But AI systems have limitations. Fully automated cybersecurity can miss critical alerts or fall victim to adversarial attacks where cybercriminals manipulate algorithms to bypass detection or flood systems with false positives.

The strongest defense combines AI’s speed with human judgment. AI handles high-volume alerts and spots patterns across massive datasets. This frees security professionals to tackle complex threats requiring strategic thinking and context.

Human error still causes most breaches. Phishing attacks succeed. Weak passwords persist. Security awareness gaps remain wide open. These give cybercriminals easy entry points that sophisticated AI defenses cannot fully close. Organizations need both better technology and better-trained people. Regular security training matters. Phishing simulations work. Building a culture where employees see themselves as defenders makes a difference.

AI will keep evolving. So will the threats it creates and the defenses it enables. The organizations that thrive will treat AI as a tool that amplifies human capability rather than replaces it.

Success in cybersecurity has always required adapting to change while remembering that behind every system, every decision, and every defense sits a person.

Origianl Creator: Ajay Nawani
Original Link: https://justainews.com/industries/cybersecurity/ai-and-cybersecurity-in-2026/
Originally Posted: Mon, 24 Nov 2025 09:47:01 +0000