TLDR: Agentic AI is emerging as a powerful ‘force multiplier’ in cybersecurity for both Information Technology (IT) and Operational Technology (OT) environments. Unlike traditional AI, agentic AI can autonomously plan, execute, and adapt to achieve security goals, offering real-time threat detection, pre-emptive action, and continuous learning. This technology is crucial for defending against sophisticated cyberattacks, especially in manufacturing, where IT/OT convergence has expanded the attack surface.
Agentic Artificial Intelligence (AI) is poised to transform cybersecurity across Information Technology (IT) and Operational Technology (OT) sectors, acting as a significant ‘force multiplier’ against evolving threats. Published on August 19, 2025, an article in Smart Industry highlights how this advanced form of AI moves beyond merely flagging threats to actively supervise, detect, analyze, and respond to them in real time, thereby substantially reducing vulnerabilities.
Unlike conventional AI systems that operate based on predefined rules or scripts, agentic AI pursues objectives autonomously. These systems can independently plan, execute, and adapt to achieve specific security goals with minimal human intervention. This capability is particularly vital in manufacturing, where the convergence of IT and OT has dramatically expanded the ‘attack surface,’ making industrial environments more susceptible to cyberattacks.
Key capabilities of agentic AI in cybersecurity include:
Real-time, Autonomous Threat Detection: Agentic AI continuously monitors both OT (e.g., industrial control systems, PLCs, SCADA) and IT networks. It detects anomalies in production equipment behavior and corporate systems by employing adaptive baselining, learning what constitutes ‘normal’ for specific machines and processes. This allows it to identify subtle deviations that traditional signature-based tools might miss.
Pre-emptive Action on Threat Indicators: Upon detecting unusual activities, such as suspicious PLC commands or network traffic, an AI agent can instantly isolate the affected device or network segment. This swift action helps prevent malware from spreading. The agent can also gather crucial information on the potentially infected device for human analysts and automatically revoke suspicious user or machine credentials to mitigate insider threats or compromised accounts.
Supply Chain and Firmware Integrity Checks: Agentic AI can scan firmware updates, software patches, and supplier integrations for hidden vulnerabilities or tampering before deployment, ensuring continuous verification.
Predictive Vulnerability Management: These AI systems can recommend and even schedule patching without disrupting production, by understanding machine criticality and threat likelihood. They can also conduct ‘red team’ exercises virtually, simulating attacks to probe for weaknesses and test defensive responses without impacting live operations.
Coordinated Multi-system Defense: Agentic AI can serve as a central decision-making hub, integrating data and signals from existing intrusion detection, endpoint security, and industrial safety systems to orchestrate a rapid and coordinated defense.
Continuous Learning from Global Threats: A significant advantage of agentic AI is its ability to learn and improve from past tasks and processes. It can ingest anonymized global threat feeds, learn from incidents in other manufacturing environments, and proactively apply those defenses to its own operational context.
The adoption of agentic AI is gaining momentum. Gartner reports that by 2028, at least 15% of day-to-day work decisions will be made autonomously through agentic AI, a significant leap from 0% in 2024. While concerns exist about malicious actors potentially leveraging agentic AI for scalable attacks, such as polymorphic code generation and synthetic identity fraud, the technology also offers a crucial advantage for defenders. Experts at RSAC 2025 noted that 59% of organizations surveyed were already working on integrating Agentic AI into their cybersecurity defenses, indicating a growing recognition of its potential to level the playing field against sophisticated cyber adversaries.
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This technology promises faster detection and response times, reduced burnout for security analysts by automating routine tasks, and enables mid-market security teams to enhance their capabilities, bridging the gap with larger enterprises. The battle of intelligent agents is upon us, with ‘defender AI’ becoming essential to combat ‘attacker AI’ and ensure robust cybersecurity.
