What Are the Predictions of AI in Cybersecurity?

According to Gartner, by 2029, agentic Artificial Intelligence (AI) will autonomously handle 80% of routine customer service inquiries, cutting operational costs by 30%.

Unlike earlier AI models that focused on generating responses or summarizing conversations, agentic AI marks a shift toward systems capable of independently executing tasks. This shift will redefine service interactions, with both customers and organizations increasingly depending on AI agents and bots to automate engagement. As this trend accelerates, the future of AI in cybersecurity will similarly evolve, demanding equally autonomous and adaptive defense mechanisms to protect these advanced systems.

Why is AI in Cybersecurity Important?

AI has become a cornerstone of modern cybersecurity, playing an increasingly vital role in protecting digital infrastructures from evolving threats. While the concept of AI in security isn’t new—the first AI-powered intrusion detection systems date back to the 1980s—the exponential growth of data and the rising complexity of cyber-attacks have made traditional rule-based systems insufficient. AI’s ability to process and analyze vast data sets in real time, identify subtle patterns, and make rapid decisions enables proactive defense against advanced threats that would otherwise go undetected, paving the way for predictive analytics in cybersecurity to anticipate and prevent attacks before they occur.

Gartner’s Top Cybersecurity Trends of 2025 report underscores the rising impact of generative AI (GenAI), pointing to emerging opportunities for organizations to strengthen their security strategies and adopt more flexible, scalable defense approaches. 

However, there is a dark side of the coin. AI is driving a new era of cyber threats, as revealed in Check Point Research’s AI Security Report 2025. The report exposes how malicious actors are leveraging AI not only to enhance their attack methods but also to scale them at unprecedented levels. Key areas of concern include: autonomous deepfakes and impersonation, jailbroken LLMs and emerging “Dark AI” models, automated malware generation and data harvesting, deceptive AI platforms spreading GenAI-driven disinformation, and the growing risk of data leaks from unregulated corporate AI usage.

With attackers continually developing more sophisticated tactics, the need for intelligent, adaptive, and automated security solutions is more urgent than ever. AI meets this demand by reducing human error, speeding up threat detection, and allowing for swift response measures, thereby helping organizations mitigate risk more effectively.

A 2024 Gartner poll found that 42% of respondents view data privacy as the leading concern when it comes to GenAI. Yet, many organizations still lack clear strategies to manage privacy risks, especially as employees increasingly turn to public large language models (LLMs) for general-purpose tasks. Addressing this gap is essential to the future of AI in cybersecurity.

At SOC Prime, we’ve built our AI SOC ecosystem around a privacy-first architecture, empowering organizations with full control over their data. Our cybersecurity and AI solutions enable organizations to decide what information to share or whether to share it at all. By utilizing purpose-fit models like META’s LLaMA and OpenAI’s GPT for specific tasks, we ensure AI interactions are both secure and transparent.

Moreover, the ongoing talent shortage reflects a lack of deep expertise and overall low maturity among cybersecurity professionals. The 2024 Voice of the CISO report highlights that nearly 74% of CISOs see human error as the industry’s most pressing vulnerability. The AI SOC ecosystem addresses this challenge by combining human intelligence with AI-powered insights, enhancing detection speed and accuracy, improving the efficiency of engineering teams, and reducing risks tied to human error.

Network Security and AI

Traditional network security tools, such as firewalls and intrusion prevention systems, struggle to keep up with the speed, volume, and complexity of the modern-day cyber threat landscape. This is where AI transforms the game. Rather than relying solely on static rules or signature-based detections, AI enables a shift from reactive defense to predictive, adaptive security. By analyzing massive volumes of real-time and historical network data, AI-powered systems can identify patterns of malicious behavior, detect zero-day exploits, and surface anomalies that human analysts or legacy tools might overlook. AI enhances network security by continuously monitoring network traffic, detecting anomalies, and recognizing potential threats in real time. 

Machine learning (ML) models trained on diverse data sets can identify both known and unknown attack vectors, including zero-day exploits and advanced persistent threats (APTs). Unlike traditional approaches, which often generate high volumes of false positives, AI can drastically reduce alert fatigue by continuously learning and refining its detection algorithms based on feedback and contextual signals.

Another strength of AI in network security is its scalability. As organizations adopt cloud-first architectures, expand remote work environments, and manage complex multi-vendor ecosystems, their attack surfaces multiply. AI offers the capability to continuously monitor this expanded footprint across on-prem, hybrid, and cloud networks at machine speed and with precision. For example, AI-driven threat detection platforms can correlate traffic anomalies across endpoints, VPN connections, and SaaS usage, flagging sophisticated attacks such as command-and-control communication, credential abuse, or lateral movement in real time.

Furthermore, AI augments human analysts by integrating into SIEM and XDR systems. These integrations allow security teams to benefit from automated triage, threat scoring, and prioritized incident investigation. AI can also power autonomous response mechanisms, such as isolating infected endpoints or throttling suspicious traffic, reducing the time to contain and remediate threats.

AI can also automate threat detection and response across complex networks, helping security SOCs reduce workload and improve reaction times. Integrating AI in network security solutions is essential for defending against dynamic, fast-moving attacks and minimizing the attack surface across distributed infrastructures. 

These capabilities underscore the growing importance of cybersecurity predictive analytics, where AI enables early detection and proactive mitigation of threats across dynamic digital environments.

Data Analytics in Cybersecurity

One of AI’s most transformative roles in cybersecurity lies in data analytics. AI systems can process massive volumes of information from logs, endpoints, user behavior, and threat intelligence feeds, making it possible to uncover threats that would otherwise remain hidden. Through advanced analytics, AI helps security teams prioritize alerts, understand attack patterns, and allocate resources more effectively.

ML algorithms enhance this process by learning from historical incidents and adapting to new behaviors, which illustrates an essential aspect of how the use of AI in cybersecurity is being improved to stay ahead of evolving threats. This adaptive capability is critical for preempting future attacks and supporting a proactive security posture. Moreover, by automating the analysis process, AI reduces the time and labor traditionally required, thereby increasing overall operational efficiency.

At SOC Prime, we harness market-leading large language models like Llama, along with purpose-built AI/ML models designed to power up cybersecurity operations:

• RAG LLM Model: Powered by RAG database with SOC Prime’s unique collection of 500K+ rules mapped to 11K metadata labels, this LLM model enables context-enriched detection rule generation from raw CTI data.
• MITRE ATT&CK® Tagging Model: Building on our innovation of tagging Sigma rules with ATT&CK introduced back in 2018, this model automates precise ATT&CK (sub)technique tagging. It is trained on the world’s largest dataset of over 50,000 rules & queries, including native SIEM, EDR, and Data Lake queries, Sigma rules, and top-quality translations.
• Query Language Detection ML Model: Trained on SOC Prime’s 500K+ multilingual set of detection rules, this model automatically detects the language of detection rules across 44 SIEM, EDR, and Data Lake formats.

Cryptography and AI

AI also holds promise in the field of cryptography, where it can both strengthen and challenge traditional methods. On one hand, AI can enhance encryption algorithms by identifying weaknesses and automating cryptanalysis to ensure more robust data protection. On the other hand, the same power could potentially be misused to break cryptographic defenses.

Emerging research suggests that AI may contribute to the development of quantum-resistant cryptographic techniques, which will be crucial as quantum computing begins to reshape the cybersecurity landscape. Thus, the intersection of AI and cryptography presents both opportunities and responsibilities for future innovation.

The Current State of AI in Cybersecurity

Today, many organizations are already integrating AI into their security frameworks. A recent survey by Cybereason found that 86% of participants already incorporate AI tools in their cybersecurity strategies, reflecting the growing importance of cybersecurity in artificial intelligence.

For instance, SOC Prime’s Uncoder AI acts as a private non-agentic AI co-pilot that helps security practitioners automate and enhance detection engineering workflows end-to-end. For the majority of AI-powered features, Uncoder AI uses Llama 3.3 customized for detection engineering and threat intelligence processing. This model operates entirely within SOC Prime’s SOC 2 Type II-compliant private cloud, ensuring full control over data, strict privacy, and IP protection.

With Uncoder, defenders can perform fast IOC sweeps with automated IOC-based query generation, seamlessly convert Sigma rules into 48 SIEM, EDR, and Data Lake languages, or perform instant cross-platform translation across 11 language formats. Uncoder AI is also a powerful assistant for AI-powered query generation, automated CTI enrichment, ATT&CK tagging, and rule verification.

AI’s current capabilities include automated malware analysis, phishing detection, behavioral monitoring, and real-time threat response. Despite these advancements, challenges remain, such as the high cost of implementation, the need for skilled personnel, and ethical concerns surrounding decision-making autonomy. However, the cost of inaction is often greater, as breaches can result in far more significant financial and reputational damage.

AI Integration with Emerging Technologies

The future of AI in cybersecurity is closely linked to its integration with other emerging technologies. For instance, combining AI with blockchain can improve data integrity and transparency in security operations. AI-driven automation in SOAR platforms enhances incident response capabilities by reducing manual intervention.

Furthermore, AI is expected to play a critical role in autonomous defense systems, capable of identifying and neutralizing threats without human oversight. As 61% of organizations express interest in adopting AI-driven automation, it’s clear that the landscape is shifting toward more intelligent, responsive security ecosystems.

As cyber threats grow more sophisticated and pervasive, AI stands at the forefront of the defense strategy. From bolstering network security and enhancing data analytics to advancing cryptographic methods and integrating with next-gen technologies, AI is fundamentally transforming cybersecurity. By continuing to invest in AI and fostering ethical, skilled implementation, organizations can stay ahead of cybercriminals and build a more secure digital future.

Striving to keep up with the modern-day ever-expanding attack surface, the global cybersecurity industry continues to face a significant talent shortage. To stay ahead, SOC teams require smarter, more adaptive tools—not only to assist experienced professionals but also to accelerate the development of Tier 1 and Tier 2 analysts. Explore how security teams can put AI to work in their day-to-day operations with Uncoder AI, an AI co-pilot for detection engineering from an exclusive SOC Prime webinar. Learn from practical use cases, showing how to automate and enhance your detection engineering end-to-end—from rule logic development and validation to optimization and documentation—accelerating workflows and improving coverage. As organizations continue to evolve their cyber defenses, solutions like Uncoder AI represent a key step forward in shaping the future of AI in cybersecurity.