The Evolution of Agentic AI in Security Operations

Over the past five years, artificial intelligence applications to security have witnessed rapid development. Agentic AI systems have begun augmenting physical security (such as surveillance, threat detection and border control) as well as cybersecurity operations such as network monitoring, incident response and automated threat mitigation. They even autonomously carry out complex security functions. Agentic AI refers to AI that exhibits some degree of autonomy or "agency". Such systems are capable of making decisions and taking actions with limited human interference (See "What Is Agentic AI and Its Role in Security Operations | Google Cloud Blog). At RSAC 2025 in particular, agentic AI made its debut. This paper offers an in-depth examination of agentic AI's rise to prominence in security from 2019-2024, covering definitions, real world deployments across industries, quantitative market and performance data, notable technological milestones as well as ethical considerations that arise with these developments. Reliable sources including government reports, academic studies and industry research provide us with insight into this transformative trend. 

Defining Agentic AI in Security 

Agentic AI (or Agentic Intelligence, Ai) is an advanced form of artificial intelligence characterized by autonomy in decision making and goal-directed behavior (What Is Agentic AI: Exploring Its Role in Security Operations). Unlike traditional forms of automation which often follow preset rules or only assist human operators - agentic AI systems can independently perceive their environment, reason through tasks, execute them to meet security objectives independently while adapting strategies dynamically based on real time data rather than static pre-programmed responses (What Is Agentic AI: Exploring Its Role in Security Operations). 

Key features that differentiate agentic AI from conventional security AI include: 

• Autonomy: Operates without constant human oversight, carrying out tasks end-to-end (Agentic AI: Exploring its Role in Security Operations); traditional security AI could flag an anomaly for human investigation while agentic AI would investigate and decide upon response actions on its own (with human approval or oversight as a backup plan). 

• Goal-Oriented and Adaptive Behavior: Agentic AI goes beyond static rules by adopting goal-oriented and adaptive behaviors to pursue higher level objectives (e.g. "contain any network intrusion") by dynamically selecting or sequencing actions to reach them (What Is Agentic AI: Exploring Its Role in Security Operations). Furthermore, agentic AI adjusts with new information while learning from outcomes as time goes on - something static algorithms cannot do. 

• Context Awareness: Agentic AI interprets data contextually to make informed decisions (What Is Agentic AI: Exploring its Role in Security Operations). For example, agentic AI monitoring network traffic could identify which anomalies really indicate threats by correlating multiple sources of data while traditional systems might just trigger on one indicator.  

• Collaborative Reasoning: Multiple agents work collaboratively in many implementations. For instance, one might handle alert triage while others focus on malware analysis or threat hunting, with all agents sharing information (What Is Agentic AI In Cybersecurity | Balbix; The Dawn Of Agentic AI At RSAC 2025 | Google Cloud Blog). This multi-agent approach makes complex distributed security problems simpler to tackle simultaneously with greater scalability and robustness compared to single purpose AI tools of old. 

Overall, agentic AI extends beyond assistive AI; previous generations of security AI focused mostly on decision support or automating narrow tasks. Agentic AI on the other hand can identify threats independently and determine responses without human oversight (The Emergence of Agentic AI in Security Operations at RSAC 2025 | Google Cloud Blog). Agentic AI represents a monumental shift: instead of providing alerts or recommendations alone, agentic AI is now capable of performing routine security tasks end-to-end and freeing human analysts to focus on complex investigations requiring their judgment (The Dawn of Agentic AI in Security Operations at RSAC 2025 | Google Cloud Blog). 

Agentic AI in Physical Security Operations 

Physical security systems traditionally rely on cameras, sensors and human guards to detect threats. Recently though, agentic AI is starting to transform these operations with advanced video analytics and autonomous robotic agents. 

AI-Powered Surveillance: Modern surveillance systems increasingly integrate artificial intelligence (AI) capabilities in cameras and monitoring software for real-time object recognition, anomaly detection, and predictive analytics capabilities. This allows real-time recognition of objects or even anomaly detection with predictive capabilities for optimal efficiency and timely results. Hanwha and Axis have recently begun including deep learning models into their CCTV cameras to enable features like automated facial recognition, license plate reading, and detection of suspicious activities (The Rise of AI in Physical Security Industry - Kenton Brothers Systems for Security). Artificial Intelligence analytics excel far beyond traditional motion detectors - they can distinguish between routine activities and real threats more reliably than their predecessors - significantly decreasing false alarms from harmless sources like shadows or animals (The Rise of AI in Physical Security Industry - Kenton Brothers Systems for Security). As such, security teams can focus their resources more quickly on real incidents, rather than investigating false alerts. Notably, AI-enhanced surveillance can instantly cross-reference faces or vehicles against law enforcement databases in real time to flag persons of interest or stolen cars and thus improve response times in critical scenarios (The Rise of AI in Physical Security Industry - Kenton Brothers Systems for Security). 

Autonomous Patrol Robots and Drones: Perhaps the most visible change in physical security is the deployment of autonomous security robots and aerial drones as “agents on patrol.” These robots serve as tireless sentries, using AI to navigate environments, detect anomalies, and even interact with humans. Ground-based security robots – such as the Knightscope K5 model – are now operating in malls, campuses, warehouses, and public spaces, augmenting or replacing human patrols. The K5, for instance, is a fully autonomous robot that roams 24/7 with no human control; it uses an array of sensors and AI software to observe its surroundings, detect irregularities, and deter crime with lights, alarms, and voice messages (More AI Powered K5 Robots Deployed in Washington - Knightscope Autonomous Security Robots & Emergency Communication Services). These robots provide a conspicuous security presence and can gather high-definition video and other data to aid investigations (More AI Powered K5 Robots Deployed in Washington - Knightscope Autonomous Security Robots & Emergency Communication Services). They are already in real-world use – in 2024, Knightscope robots were deployed to bolster safety at a Washington state casino, illustrating how private sector security is adopting AI patrol bots alongside human guards (More AI Powered K5 Robots Deployed in Washington - Knightscope Autonomous Security Robots & Emergency Communication Services). 

(More AI Powered K5 Robots Deployed in Washington - Knightscope Autonomous Security Robots & Emergency Communication Services) AI-driven robots can patrol premises, detect unauthorized activity, and even engage with intruders via audio messages. They operate continuously, extending surveillance capabilities while reducing the burden on human security staff. (More AI Powered K5 Robots Deployed in Washington - Knightscope Autonomous Security Robots & Emergency Communication Services) 

As well as wheeled robots, quadruped ("robot dogs") and aerial drones have increasingly become security agents (HowToRobot). Security robots offer agents for automating safety monitoring operations at work or home (HowToRobot). Drones offer rapid aerial surveillance for large perimeters - for instance, they can automatically dispatch to alarm locations while streaming live video footage to security teams. Tested successfully at critical infrastructure sites like airports is this trend: Singapore Changi Airport recently deployed autonomous patrol robots into one terminal to supplement security staff (see Security robots: Automating safety and monitoring operations | HowToRobot). Border security and defense applications show even stronger signs of effectiveness. U.S. Homeland Security officials recently conducted trials utilizing autonomous robotic "dogs", equipped with cameras and sensors, to patrol remote sections of border. Their aim: extend surveillance in dangerous or tedious terrain (Police Drones and Robots: 2022 in Review | Electronic Frontier Foundation). Militaries have also explored stationary autonomous gun turrets and sentry robots with artificial intelligence capabilities that use AI to identify intruders; though this raises ethical considerations when given lethal capabilities (Secretary-General's Remarks Before Security Council on Artificial...). (Police Drones and Robots 2022 in Review | Electronic Frontier Foundation). 

Benefits and Impact: Integrating AI technology into physical security offers several distinct advantages. First and foremost, robotic security provides consistent 24/7 coverage without fatigue; robots and AI-enhanced cameras keep watch around the clock in contrast to human guards (Security robots: Automating safety and monitoring operations | HowToRobot). Second, these systems offer enhanced data analysis capabilities. By detecting subtle patterns (such as someone loitering in an otherwise empty area) which humans might overlook (Security robots: Automating safety and monitoring operations | HowToRobot), security robots help detect subtle anomalies humans might overlook (see Security robots: Automating safety and monitoring operations | HowToRobot). Thirdly, autonomous agents provide faster responses to incidents; for instance, security drones can respond immediately upon hearing of an alarm and provide visual confirmation of break-in before guards even arrive on site (Security Robots: Automating Safety and Monitoring Operations | HowToRobot). Over time, cost efficiencies also become apparent: while initial investments may be high, robots do not require salaries or breaks like human workers do and fleets of AI cameras can often be monitored with less personnel required than before (Security robots: Automating safety and monitoring operations | HowToRobot). Due to these advantages, security robots have gained widespread adoption - from airports to corporate campuses; organizations seeking tightened coverage now relying heavily on AI-powered security devices for added protection coverage. 

Case Studies Across Sectors: Agentic AI in physical security is being applied across various industries: 

• Critical Infrastructures: Utility and industrial sites are turning to AI surveillance to guard against intrusions and sabotage, including power companies' deployment of drones equipped with thermal cameras to automatically patrol pipelines and transmission lines for maintenance as well as to detect unapproved activity; modern security robots often come equipped with environmental sensors capable of sensing chemical leakage or high temperatures - providing another layer of critical infrastructure protection against potential safety risks as well as threats (The Rise of AI in Physical Security Industry by Kenton Brothers Systems for Security). 

• Commercial Enterprises: Casinos, shopping malls, and tech campuses have increasingly adopted patrol robots to detect intruders while acting as deterrents by simply their presence. One notable deployment occurred at Singapore Changi Airport where autonomous robots assist human officers with monitoring passenger areas for potential security incidents (Transportation sector; HowToRobot). Retail chains have begun testing AI camera systems which monitor store aisles to automatically detect shoplifting or any suspicious behaviours in real-time, alerting security before thieves exit - an impressive deployment occurred there as well. 

• Law Enforcement and Public Safety: Law enforcement and public safety agencies have conducted experiments using AI-powered surveillance towers and robots for monitoring public spaces such as parks. In certain cities pilot projects used mobile security robots for park patrol and parking lot monitoring. However, public-sector use lags behind private usage due to regulatory and community considerations. One controversial proposal in 2022 involved equipping drones with nonlethal weaponry such as Tasers so as to automatically respond to active shooter situations at schools (Police Drones and Robots: 2022 in Review | Electronic Frontier Foundation). Although that particular project was suspended following resignations by its ethics board (Police Drones and Robots: 2022 in Review | Electronic Frontier Foundation), it serves to demonstrate how autonomous or semi-autonomous response systems may play a significant role in law enforcement operations. 

Market Growth: Market statistics demonstrate the rapid adoption of Artificial Intelligence for physical security applications. Industry reports reveal that global spending on AI-powered security solutions spanning both physical and cyber domains is rising quickly, forecasted to reach $71 billion by 2027 with compound annual compound annual growth exceeding 23% (The Rise of AI in Physical Security Industry by Kenton Brothers Systems for Security). Focusing solely on robotics, the security robots market was estimated at an estimated value of $16.5 billion by 2023 and projected to increase at approximately 15% annually through 2030 (Security Robots Market Size Share & Trends Report 2030). This growth is fuelled both by advances in AI and autonomous navigation as well as practical needs - organizations face shortages in security personnel and are turning towards robotic alternatives (Security Robots Market Size, Share & Trends Report 2030). As one market report noted, retail, hospitality and manufacturing industries with limited staff resources are driving robot adoption for improved response times and consistent security protocols (Security Robots Market Size Share and Trends Report 2030). Overall physical security systems are becoming smarter and more automated with AI serving as their core component; agentic AI at the heart of new surveillance and guard systems is helping ensure improved physical protection measures are being put in place. 

Agentic AI in Cybersecurity Operations 

Since 2017, AI's role within Security Operations Centers and defensive cyber tools has seen dramatic expansion. While security teams had traditionally struggled with an overwhelming volume of alerts, fast-evolving threats, and limited analysts; Agentic AI now offers solutions by automating detection, analysis, remediation of cyber threats with unprecedented speed and sophistication. 

Autonomous SOC Workflows: Traditional security operations centers (SOCs) depend upon analysts for triaging alerts, investigating incidents, and taking appropriate actions (like blocking an IP or isolating machines). Nowadays, agentic AI "analysts" have taken over many of these duties more effectively. Tech industry leaders have introduced AI-powered security co-pilots and agents that integrate seamlessly into SOC workflow. Google presented in 2025 their vision of an "agentic SOC", consisting of several AI agents working semi-autonomously in security operations (The Dawn of Agentic AI in Security Operations at RSAC 2025 | Google Cloud Blog). One such agent is an alert triage agent: when new alerts come in, this agent gathers context (logs, threat intelligence, system data) before making its determination as to whether an alarm represents an actual threat or false positive and provides its verdict and supporting evidence (The Dawn of Agentic AI in Security Operations at RSAC 2025 | Google Cloud Blog). Continuous automated investigation can significantly decrease Tier-1 analysts' workload of manually screening hundreds of alerts per day (The Dawn of Agentic AI in Security Operations at RSAC 2025 | Google Cloud Blog). An additional example would be an autonomous malware analysis agent which can autonomously reverse engineer suspicious files, attempt de-obfuscation techniques and determine whether malware exists (The Dawn of Agentic AI in Security Operations at RSAC 2025 | Google Cloud Blog), tasks which would normally require human analysis for. 

(Google Cloud Blog, 2025 RSAC Conference). Conceptual Model of an Agentic Security Operations Center (SOC). Multiple Artificial Intelligence agents manage various stages of incident handling from alert triage through investigation to response while cooperating with data systems and human analysts - from alert triage through investigation, response coordination with human analysts as well as autonomous handling for routine threats while supporting experts with complex hunting/detection engineering efforts (Microsoft Unveils Copilot Agents with New Protections | Microsoft Security Blog). (The Dawn of Agentic AI in Security Operations at RSAC 2025 | Google Cloud Blog). (Microsoft Unveils Microsoft Security Copilot Agents with New Protections | Microsoft Security Blog). 

Microsoft has also integrated agentic AI into their security platform. Microsoft released Security Copilot as an AI assistant for defenders in 2023; by 2024-2025 it had evolved to include dedicated AI agents for tasks like email triage, identity management and data protection (Microsoft Unveils Microsoft Security Copilot Agents And New Protections For AI | Microsoft Security Blog). One such agent can quickly handle phishing alerts - parsing suspicious emails and extracting indicators before orchestrating quarantine of malicious messages - freeing human analysts to focus on more complex threats (Microsoft Unveils Microsoft Security Copilot Agents And New AI Protections | Microsoft Security Blog). Microsoft stresses the need to scale defenses using AI agents with attacks generating billions of signals (over 30 billion phishing emails were detected in 2024 alone!), scaling is now essential in keeping pace with attacks (Microsoft Unveils Security Copilot Agents And New AI Protections | Microsoft Security Blog). 

Beyond tech companies' offerings, many organizations have utilized AI systems for cybersecurity purposes. A notable case is NVIDIA's internal security team which built an agentic AI agent for automating vulnerability management. This agent autonomously triages newly discovered software vulnerabilities by collecting all relevant information (i.e. if a system has been affected, do exploits exist, etc.) and providing it to analysts in a report format. NVIDIA's team estimated it saved 5 to 30 minutes per vulnerability of analyst time when deployed at scale, translating to several hours saved each week when viewing 10+ vulnerabilities each time (Advancing Cybersecurity Operations with Agentic AI Systems | NVIDIA Technical Blog). That time could then be put toward higher-priority tasks such as identifying critical flaws more effectively - an impressive real world result which shows how agentic AI systems can boost efficiency by taking on repetitive analysis work for efficient cyber operations operations. 

Threat Detection and Response: Agentic AI has proven its worth when it comes to quickly spotting threats faster and responding immediately in cases of incidents that arise. AI-powered security monitoring tools (such as modern IDS/IPS systems and behavior analytics tools ) use machine learning techniques to detect unusual patterns in network traffic, user behaviors or system logs that might signal attacks on our infrastructures. Studies have demonstrated that AI-powered intrusion detection systems (IDSs) often demonstrate higher detection rates and reduced false positive rates compared to rule-based IDSs, often at reduced false positive rates ((PDF)AI Vs Traditional IDS: Comparative Analysis of Real World Detection Capabilities)((PDF) AI Vs Traditional IDS Traditional IDS: Comparative Analysis of Real-World Detection Capabilities). Simply stated, AI can detect more subtle yet unknown attack patterns that traditional signature-based tools miss with greater accuracy and greater speed. Deep learning models have demonstrated accuracy rates between 98%-999% on benchmark datasets for detecting new malware or network intrusions with deep neural net models (Intrusion Detection: A Comparative Study of Machine Learning...). AI systems offer unprecedented data processing speeds that human operators or legacy tools simply cannot match. Studies indicate that approximately 70% of organizations report AI as highly effective at detecting previously undetectable cyber threats (The Impact of AI on Cyber Security: Key Stats & Protective Tips | BD Emerson), showing just how machine learning has increased threat visibility. 

Agentic AI truly shines at responding quickly when threats emerge - this is what sets it apart. Autonomous response solutions can take immediate steps when threats emerge to contain damage before it spreads further. Darktrace stands as an impressive example. Their Artificial Intelligence system (Antigena) has become standard practice across multiple enterprises worldwide. Darktrace AI successfully helped stop an attempt at crypto mining malware attacks in 2022 by isolating devices with infected files - this occurred even while their human leader was away (Darktrace Artificial Intelligence Autonomously Stops Consequences of Fast-Moving Cyber Attack at Major Italian Electronics Distributor). AI immediately recognized malicious behavior and enforced a quarantine within seconds - stopping a potential attack from becoming worse. Darktrace reports that its autonomous response technology can take rapid action whenever threats are identified; their artificial intelligence reportedly prevents some attack escalations at client organizations every minute (Darktrace Artificial Intelligence Autonomously Stops Consequences of Fast-Moving Cyber-Attack at Major Italian Electronics Distributor). Speed that surpasses human response can mean the difference between an contained incident and major breach. Microsoft Security Copilot team noted how their AI assistance helps responders address incidents "within minutes instead of hours or days." (Introducing Microsoft Security Copilot: Empowering Defenders At the Front...). Such rapid responses have proven critical for mitigating damage (for instance halting ransomware attacks before they encrypt an entire network). 

Applications and Case Studies: All major industries that are targets of cyberattacks have begun adopting agentic AI for defense: 

• Financial Services: Banks and financial institutions that deal in high-value assets with stringent uptime requirements were early adopters of AI technologies for use in their SOCs. Global banks employ AI systems to monitor transaction networks and internal systems, automatically flagging and blocking any irregular behavior that arises (for instance an normally dormant account suddenly making large transfers - potentially indicative of account takeover). Apex Fintech Solutions, a financial services firm, reported that using Google AI cut down complex security rule writing from hours of analyst time down to seconds (The Dawn of Agentic AI in Security Operations at RSAC 2025 | Google Cloud Blog). This allows faster deployment of threat detection logic while lessening reliance on scarce human experts for routine updates. 

• Government and Defense: National security agencies and defense departments have invested in artificial intelligence as an AI solution to better secure critical networks. CISA recently adopted AI tools for tasks including anomaly spotting and automating threat intelligence processing (CISA Artificial Intelligence Use Cases | CISA). DARPA also initiated its AI Cyber Challenge (AIxCC) competition in 2023 to develop autonomous AI systems capable of finding and fixing software vulnerabilities without human interference (Salt Typhoon Hack Influences Final Round Of DARPA's AI Cyber... This signifies an increasing strategic interest in agentic AI systems capable of both responding to attacks quickly as well as hardening systems proactively without human involvement.). 

• Healthcare and Critical Infrastructure: Hospitals and utilities often employ smaller security teams but face severe consequences should any attacks succeed (for instance ransomware hitting hospitals). These industries have begun adopting AI-powered monitoring that can detect infected devices or trigger incident protocols automatically. Example of agentic AI being deployed at water treatment plants : It could detect suspicious commands in its control system and immediately lock down compromised workstations to stop possible acts of sabotage before it's too late. Automated incident response in industrial control system networks, known as industrial control system or ICS networks, remains in its early stages but under active development due to their criticality (A survey on safeguarding critical infrastructures: Attacks and AI security...)(Securing Critical Infrastructure in an Age of AI (CSET). 

Market and Investment Trends: AI advancement within cybersecurity industry is underscored by strong market growth and investment activity. AI in cybersecurity was estimated at an approximate global market valuation between 2021-2024 of $15-30 billion; by 2030 this number is anticipated to have skyrocketed to between $133-135 billion (Impact of AI on Cyber Security: Key Stats & Protective Tips | BD Emerson). At an estimated annual compound growth rate of 20%+, IT security startups offering AI-centric products are also seeing unprecedented investment activity. One report showed that AI-focused cybersecurity funding nearly doubled from 2023-2024, rising from around $181 million in 2023 to about $370 million a year later (Rhymetec). Security vendors are investing heavily in artificial intelligence research; major firms including IBM, Microsoft, Google and several startups have all pledged to make "autonomous SOC" capabilities one of their core selling points by 2024-2025. Industry consensus dictates that AI needs to be leveraged heavily in order to effectively counter today's cyber threats. By 2024, two-thirds of IT/security professionals worldwide had tested or implemented AI security technologies (BD Emerson's Impact of AI on Cyber Security: Key Stats & Protective Tips |) as an industry trend - evidence that its implementation had reached mainstream acceptance levels. 

Quantifying some benefits: organizations using agentic AI have reported significantly improved performance metrics and intrusion detection AI systems have achieved detection rates as high as 99.99 on complex attacks while simultaneously decreasing false alarm rates ((PDF) AI Vs Human Intercept). Comparative Evaluation of Real World IDS Capabilities)" AI Vs Traditional IDS: Comparative Examination of Real-World Capabilities). Incident response has seen automation drastically speed up threat containment compared to prior methodologies (Darktrace Artificial Intelligence Autonomously Stops Consequences of Fast-Moving Cyber Attack at Major Italian Electronics Distributor.). One survey discovered that 70% of organizations credit AI for improving response times and security effectiveness. More than half have deployed it as part of an AI staff augmentation strategy (BD Emerson: Impact of AI on Cyber Security: Key Stats & Protective Tips | BD Emerson).  

Technological Breakthroughs (2019–2024) 

The period from 2019 to 2024 has witnessed several technological breakthroughs that enabled the rise of agentic AI in security: 

• Advances in AI Models: Deep learning models have made tremendous advancements, especially within computer vision and natural language processing fields. Physical security applications benefit immensely from improved computer vision models (e.g. YOLO/ResNet/etc.). Real-time object and behavior recognition has now become practical around 2018-2020 in surveillance footage, providing real-time object and behavior identification within minutes of upload. Large Language Models (LLMs, such as GPT-3 in 2020 and GPT-4 in 2023) have revolutionized cybersecurity, providing powerful reasoning engines capable of analysing logs, code or threat intelligence texts conversationally. Together with AI agents, LLMs enable systems which are capable of quickly parsing complex scenarios and even producing code or scripts to address threats. An LLM agent, for instance, can read an incident report and suggest remediation steps or generate firewall rules automatically to block attack vectors - capabilities which were only vaguely present prior to LLM technology's advent. This combination of language understanding with security expertise (as evidenced in products like Microsoft Security Copilot) started manifesting around 2023 (Introducing Microsoft Security Copilot: Empowering Defenders at the Edge...). 

• Agent Frameworks and Architectures: New software frameworks have emerged to implement agentic AI. Between 2022-2023, research projects focused on designing AI agents capable of planning and acting autonomously. Reinforcement learning and planning algorithms became widely utilized for security applications (e.g., automating penetration tests or adaptive defense strategies). Industry giants Google and NVIDIA released toolkits specifically tailored towards building multi-agent systems. NVIDIA recently unveiled their Agent Intelligence Framework as a way of simplifying cybersecurity agent creation with modular tools (Advancing Cybersecurity Operations with Agentic AI Systems | NVIDIA Technical Blog). Academic work on multi-agent systems and hierarchical planning (such as Hierarchical Task Networks for security tasks (What Is Agentic AI in Cybersecurity | Balbix)) provided blueprints for more complex goal-driven AI in SOC environments. By adopting such frameworks, communities were able to prototype and deploy agentic workflows much quicker, further spreading this technology's use across organisations. 

• AI in Security Platforms: Major security vendors began embedding AI technology deeply into their platforms during this period, marking a notable step forward towards mainstreaming the technology. IBM's security division had experimented with Watson AI for security use before 2023-24 when IBM and others started touting "autonomous security operations" as being available and real. IBM released in 2025 its cutting-edge agentic AI capabilities for autonomous SOC operations (The dawn of agentic AI in security operations at RSAC 2025 | Google Cloud Blog).) 2023's RSA Conference witnessed multiple demonstrations of AI agents for security use cases, suggesting an industry consensus to adopt this paradigm shift (The Dawn of Agentic AI in Security Operations at RSAC 2025 | Google Cloud Blog). Microsoft Security Copilot in March 2023 with GPT-4 was an epic moment: for many enterprises it marked their introduction of an AI assistant into their security workflow, while its evolution into autonomous agents showed where things were heading (Microsoft Unveils MS Copilot Agents And New Protections For AI | MS Security Blog). By 2024/25 both Microsoft and Google had delivered agentic AI features into their products: this marks an impressive accomplishment that effectively validates agentic AI as the future of SOC tooling. 

• Autonomous Cyber Defense Challenges: One significant event was the DARPA Cyber Grand Challenge held in 2016 (just ahead of our five year window), which demonstrated fully autonomous cyber defense could indeed work within contest settings - setting an important foundation for our continued efforts in cyber security. DARPA organized its AI Cyber Challenge with tech firms by 2023; their goal was to produce AI agents capable of finding and patching vulnerabilities within critical infrastructure code (Salt Typhoon used hacking techniques to influence final round of DARPA AI Cyber...). The announcement of this challenge, with significant prizes and prominent participants involved, marks another landmark achievement of technology progress: autonomous cyber "blue teams" were science fiction only 10 years ago! We expect that by 2024 this competition will push agentic AI capabilities even further - including automated vulnerability remediation which would become revolutionary tool for defense forces if implemented successfully. 

• Physical Security Robotics: On the physical front, breakthroughs in autonomous navigation and sensor fusion technologies have made security robots viable options for use today. Improved Lidar technology, cheaper and faster GPUs for on-board processing, and superior obstacle avoidance algorithms like AI SLAM have allowed Knightscope-style robots to reliably patrol dynamic environments such as elevators or parking lots filled with cars. Integration of robotics advances with artificial intelligence-powered detection methods such as computer vision for intruder detection or audio analytics for gunshot detection was completed during this period. Notably in 2023, one of the leading online retailers (Amazon) unveiled an advanced version of Astro home robot suited for commercial security patrols (Security Robots Market Size, Share & Trends Report 2030). This signals an important milestone towards making robotic technology accessible and widespread - something other tech giants must back. 

• Regulatory and Ethical Frameworks: Though this development may not qualify as technological progress in itself, recent years saw the creation of frameworks governing AI use for security applications. Governments and international bodies began setting guidelines for AI use in critical infrastructure and military environments (Political Declaration on Responsible Military Use of Artificial...) (Setting the Standard: DHS Debuts First-of-Its-Kind AI Safety Initiative...). Late 2023, the U.S. government issued an Executive Order emphasizing safe and secure AI development with emphasis placed upon careful deployment in security contexts ([PDF] Federal-Cybersecurity-RD-Strategic-Plan-2023.pdf). Meanwhile in 2024, European and United Nations discussions regarding autonomous weapons and surveillance AI gained steam (Secretary-General Remarks to Security Council: On Artificial...) (Time is Now to Discuss Autonomous Weapons...). These efforts, while not technically groundbreaking breakthroughs, represent milestones towards overseeing technology growth while protecting users - possibly shaping its design with human "kill switches" on autonomous security robots or mandating transparency and audit logs in AI decisions - something CISA has championed (CISA Artificial Intelligence Use Cases | CISA). 

In summary, by 2024 agentic AI in security is no longer an experimental concept; it stands on the shoulders of breakthroughs in AI research, has been catalyzed by large industry players incorporating it into products, and is supported by a maturing ecosystem of tools and policies. This confluence of innovation has brought us into an era where an “agentic SOC” or an autonomous security robot is not a futuristic idea but an operational reality in many places. 

Challenges and Ethical Considerations 

Though AI offers great promise in security domains, the implementation of agentic AI poses numerous difficulties and ethical considerations that must be resolved as organizations grant more decision-making power to AI agents. Organizations must consider issues of trustworthiness, accountability and risk. 

Reliability and Accuracy: One key challenge facing AI agents lies in making correct decisions. Failing to recognize threats (false negatives), or false alarms or taking unwarranted action can both have serious ramifications for both themselves as well as those around them. An AI used for cybersecurity risks misclassifying harmless system updates as malicious, and therefore closing off an essential server, which could prove disastrous in sectors like healthcare and severely compromise patient lives. Meanwhile, missing genuine threats undermines its purpose altogether. Studies show AI systems can achieve high degrees of accuracy; however, they should not be seen as completely reliable solutions. There have been warning signs - for instance, early facial recognition systems used by police resulted in misidentifications that led to unwarranted arrests of people of color due to algorithmic bias (see Detroit Changes Rules on Police Facial Recognition after Arrest of Black Man | Detroit | The Guardian for details). Detroit police arrested an innocent man after using facial recognition technology incorrectly to match him to surveillance footage in 2020; such incidents illustrate how accuracy issues can translate to ethical and legal challenges related to violation of individual's rights in physical security systems. Ensuring algorithm fairness through extensive testing is vital before trusting AI judgments; organizations are now starting to require bias audits before using AI judgements on individuals for physical security applications - such as when AI flags someone as suspect for example. 

Human Oversight Vs Autonomy: Finding an acceptable balance between AI autonomy and human control remains a contentious topic in high-stakes scenarios; fully handing control to an AI may pose risks that outweigh its advantages. Most experts support an approach known as the 'human in the loop" or "human on the loop", where an AI may take actions autonomously while being overseen or overseen by human supervisors whose job it is either to approve certain critical actions taken autonomously by AI systems, or intervene/override critical ones if required. In cybersecurity terms, AI could use automated quarantining of an infected device as a reversible action; but would not perform delete-like processes on databases without first seeking approval from human signatories. Physical security concerns involving lethal or forceful actions by robots are highly contentious - the public has already spoken out over proposals to arm robots with weaponry. San Francisco witnessed significant protest in 2022 over allowing police robots to use deadly force, leading the city to switch its policy that allowed such use (Police Drones and Robots: 2022 in Review | Electronic Frontier Foundation). Ethically speaking, most individuals believe an autonomous system should not make life-or-death decisions on its own without human oversight and confirmation. Even nonlethal actions require human oversight - for example a robot deciding to confront or detain an individual could escalate an already volatile situation should its misjudgments cause miscalculations to take place. Therefore, many current deployments keep human security officers involved via remote monitoring interfaces; robots provide eyes, ears, and limited actions, but ultimately humans make all critical calls regarding anything critical matters if necessary. 

Adversarial Threats to AI: Unfortunately, when defenders deploy AI systems, attackers often find ways to exploit or trick these systems. Agentic AI introduces new attack surfaces: an adversary may try feeding false data or triggers into its algorithms in an effort to mislead or trick it (known as adversarial examples). Researchers have demonstrated how subtly altering several pixels of an image camera can cause deep learning models to misread the scene as though there's no one present or misinterpret a license plate number as meaning nothing. Cyber attackers have employed various techniques against AI-powered assistants - specifically feeding specially tailored inputs into them which cause it to malfunction and reveal sensitive info - in an effort to compromise them and gain control. An agentic AI with access to powerful actions must be protected against manipulation. Security experts advise sandboxing these agents and restricting permissions (principle of least privilege). (What Is Agentic AI In Cybersecurity | Balbix). Integrity of data is also of vital importance: agents should verifying authenticity of information they act on to avoid being duped (What Is Agentic AI In Cybersecurity | Balbix). Network monitoring AI must ensure attackers do not falsify system logs to trick it into believing everything is normal, leading to false alarms from this kind of cyber threat. AI defenders need to implement their own safeguards. CISA guidelines note that artificial intelligence requires stringent auditability measures - every action it takes should be recorded for review (What Is Agentic AI In Cybersecurity | Balbix), so in case something unusual arises investigators can quickly trace whether its programmers attempted any illegal modifications or simply made mistakes. 

Transparency and Accountability: When an AI agent makes decisions - for instance locking user accounts due to suspicious activity or labelling an individual as being potentially harmful - who holds responsibility if that decision turns out to be incorrect? This question encompasses both legal considerations as well as ethical ones. Organizations employing agentic AI must ensure there is clear allocation of responsibility, often through review processes or explainability tools. There has also been increased interest in Explainable AI for security (XAI) purposes ((PDF) AI Vs Security). Traditional IDS: Comparative Analysis of Real-World Detection Capabilities). When an AI blocks network traffic, its reason should be provided (e.g. "blocked due to malware beaconing behavior observed on 5 hosts") so a human can review. Lack of transparency can reduce trust from operators-analysts - analysts must trust AI recommendations. Modern systems often include user interfaces that clearly demonstrate an AI's reasoning steps or evidence (Google Cloud Blog's alert triage agent provides such transparency through an audit log of how it reached its verdict (The Dawn of Agentic AI in Security Operations at RSAC 2025 | Google Cloud Blog)). Regulators could soon require this transparency; for instance, Europe's Draft AI Act leans toward mandating explanations for high-risk AI decisions. 

Concerns Over Privacy: Increased AI usage in surveillance can raise privacy issues. Cameras equipped with AI that recognize individuals or track them can breach individuals' rights to privacy if deployed improperly. AI's mass data collection capabilities (scanning every face in a crowd against watchlists, analysing behavior patterns etc) could potentially constitute intrusive surveillance if left unmonitored; due to these concerns several jurisdictions have responded by restricting or banning facial recognition usage by law enforcement officials. Public sentiment can be an obstacle: some might feel unnerved by robots or AI monitoring that look suspiciously like "Big Brother", thus needing transparency and community participation to manage. Agencies employing such tech must disclose it and its purpose, while adhering to privacy laws. Furthermore, public deployment of security robots has spurred calls for clear policies on data retention (how long are recordings stored?) Data use should only be for security-related uses and bias should be mitigated accordingly. In response, UK conducted surveys in key sectors to understand how AI was being deployed and administered within them - which highlighted their serious concern at government levels over getting it right (AI Cyber Security Survey main report - GOV.UK). As seen with our earlier example of Detroit facial recognition case settlement, this not only provided compensation to victim but also forced police to change policies - for instance no longer arresting people solely based upon AI matches (Detroit Changes Rules For Police Use Of Facial Recognition Following Arrest Of Black Man | Detroit | The Guardian). This evidenced how governance is shifting away from using AI alone as the sole arbiter of guilt or innocence. 

Integration and Skill Challenges: Practically, adopting AI systems isn't as straightforward. Organizations face issues in integrating them with existing security infrastructures. Integrating an AI SOC platform with all necessary data feeds (logs from various tools, threat intelligence sources, ticketing systems etc) is often complex. Many enterprises experience difficulties due to skills gaps - using and maintaining AI requires expertise in data science and model tuning that traditional security teams may lack, increasing risk that employees won't use or recognise when something goes amis. If staff don't fully comprehend their AI system they could misuse it or miss when something's amiss. Training may also be required and potentially new roles (some companies now employ "AI security specialists"). Additionally, the initial costs and efforts associated with deploying these advanced systems can be prohibitively expensive (Security robots: Automating safety and monitoring operations | HowToRobot), creating another barrier to smaller organizations' adoption of them. Careless planning could result in investing in AI solutions only to discover that they go unused; or worse still, produce so many results that the team still feels overwhelmed (though in different ways). Early SOC AI deployments that generated too much analytic data without clear guidance caused alert fatigue - thus the importance of best practices and change management when introducing agentic AI into operations (see: "Agentic AI In Cybersecurity: SOC Automation Led By AI Agents"). 

Overall, ethical and operational challenges do not diminish the benefits of agentic AI; rather they create conditions necessary for its successful utilization. Responsible deployment is required of this technology: its accuracy must be validated with human oversight, it should protect against adversarial manipulation and it must operate transparently with regard for privacy considerations and be supported by effective integration strategies. Maintaining trust between AI systems and security personnel collaborating together and the public relying upon these safety systems for protection is of utmost importance to their proper operation. Policymakers and industry groups are becoming more aware of these concerns, with ongoing initiatives (standards, frameworks, and laws) to ensure agentic AI in security is used ethically, legally, and effectively (see: Security robots: Automating safety monitoring operations | HowToRobot). 

Conclusion 

Agentic AI is revolutionizing security operations at every turn. From autonomous drones monitoring borders and intelligent software agents hunting cyber intruders, the transition period 2019-2024 saw these technologies go from conceptualisation to realization. As has been demonstrated herein, agentic AI systems characterized by autonomy, adaptability and goal-directed action can bring substantial advantages: faster response times, larger scale capabilities and often improved accuracy for detecting and neutralizing threats. Real deployments across defense, finance, critical infrastructure protection and law enforcement demonstrate both their potential and variety of applications; from robot patrolling an airport terminal to AI technology that detects network attacks at 3 AM without human interference. 

Quantitatively, AI's exponential market size growth and investment surge is evidence that more individuals acknowledge its role as part of future security architectures. Notable breakthroughs include the incorporation of advanced artificial intelligence models (such as LLMs ) into security teams' arsenal and multi-agent SOC framework development, both which have greatly expanded what security teams can accomplish. As one might imagine, an "AI-empowered SOC" arises with this vision of human experts working alongside AI agents that specialize in routine or analysis-intensive parts of the job. Physical security officers could similarly utilize robot surveillance as part of a layered defense approach with human officers focusing on more strategic decision-making and incident handling tasks. 

But with every transformation comes its share of challenges; over the next several years it will be as much about governance, training and optimizing agentic AI systems than about sheer technological advancement. Organizations will need to build trust in AI systems - through rigorous testing, validation and setting clear boundaries on AI actions. Ethical use will remain at the center of discussions: how best to utilize its power without infringing upon civil liberties or risking unintended harm. Reassuringly, the community is acting swiftly to address AI security - with efforts including transparent model reporting, bias mitigation research and developing standards (for instance by mandating that autonomous decisions be reviewed by humans - see Detroit case where laws changed regarding facial recognition use after unlawful arrest of Black man | Detroit | The Guardian). 

In conclusion, agentic AI in security operations seems poised to move from early adoption phase into becoming an established part of security strategy. Human-machine collaboration enables more proactive and resilient defense against threats: for instance, breaches can be contained swiftly; suspicious activities in facilities detected and addressed before becoming more dangerous threats. These outcomes have become tangible reality thanks to agentic AI being carefully developed and deployed, and documented here in this paper. When used appropriately it could dramatically improve both effectiveness and efficiency for security operations. Over the next several years, AI may become even more integrated into both physical and cyber defense strategies, providing truly holistic security approaches as well as providing frontline staff with insights from AI-powered insights and automated tools for increased protection. Security organizations that make use of agentic AI will set new benchmarks in protecting assets, infrastructure and people in an ever-evolving risk landscape (The Rise of AI in Physical Security Industry - Kenton Brothers Systems for Security). Industry stakeholders and policymakers need to use AI wisely and ethically in pursuit of safer world. 

Sources:

This research paper has synthesized information from a variety of reputable sources, including industry whitepapers (e.g., Google Cloud and Microsoft security blogs) detailing recent AI developments (The dawn of agentic AI in security operations at RSAC 2025 | Google Cloud Blog) (Microsoft unveils Microsoft Security Copilot agents and new protections for AI | Microsoft Security Blog), academic and technical studies on AI in intrusion detection ((PDF) AI Vs. Traditional IDS: Comparative Analysis of Real-World Detection Capabilities), market research data on adoption and investment trends (The Rise of AI in the Physical Security Industry - Kenton Brothers Systems for Security) (Impact of AI on Cyber Security: Key Stats & Protective Tips | BD Emerson), and case studies reported by organizations and media (such as the Darktrace incident (Darktrace Artificial Intelligence Autonomously Stops Consequences of Fast-Moving Cyber-Attack at Major Italian Electronics Distributor) and the Detroit facial recognition case (Detroit changes rules for police use of facial recognition after wrongful arrest of Black man | Detroit | The Guardian).