{"id":3027,"date":"2026-05-26T18:17:58","date_gmt":"2026-05-26T18:17:58","guid":{"rendered":"https:\/\/www.castelis.com\/?post_type=article&p=3027"},"modified":"2026-05-26T18:18:16","modified_gmt":"2026-05-26T18:18:16","slug":"ai-cybersecurity","status":"publish","type":"article","link":"https:\/\/www.castelis.com\/en\/insights-ressources\/ai-cybersecurity\/","title":{"rendered":"AI Cybersecurity: Threat or Strategic Advantage?"},"content":{"rendered":"\n\n\n

According to the ENISA Threat Landscape 2025, more than 80% of phishing emails observed between September 2024 and February 2025 used AI in one form or another. On the defensive side, the AI cybersecurity solutions market reaches $30.9 billion in 2025 according to Mordor Intelligence. The same technology feeds both camps.<\/p>\n\n\n\n

For a CISO or CIO at a mid-sized company, the question is no longer whether AI will enter the cybersecurity perimeter. It already has. The real question: how to turn it into leverage without inheriting its specific risks?<\/p>\n\n\n\n

This article describes what AI cybersecurity actually covers today, its concrete defensive use cases, the new threats it introduces, and the disruption autonomous agents represent. It also lays out a pragmatic integration framework for mid-sized companies.<\/p>\n\n\n\n

AI Cybersecurity: What Are We Really Talking About?<\/h2>\n\n\n\n

The Three Generations of AI in Cybersecurity<\/h3>\n\n\n\n

AI cybersecurity was not born in 2023 with ChatGPT. Three generations coexist today inside modern SOCs.<\/p>\n\n\n\n

The first relies on rule engines<\/strong> and signatures. Robust but blind to the unknown, they remain the foundation of legacy antivirus and IDS solutions.<\/p>\n\n\n\n

The second uses machine learning<\/strong>: behavioral analytics, anomaly detection, risk scoring. This so-called “discriminative” AI has been powering modern EDR and SIEM tools for about a decade.<\/p>\n\n\n\n

The third, still emerging, builds on generative and agentic AI<\/strong>. LLMs produce language, code and decisions, sometimes in autonomous chains.<\/p>\n\n\n\n

Generative AI vs. Agentic AI: The Critical Distinction<\/h3>\n\n\n\n

Confusing the two leads to bad architectural decisions. Generative AI produces a response to a prompt and stops there. Agentic AI chains actions: it plans, executes, observes the result, adjusts.<\/p>\n\n\n\n

An assistant that summarizes SIEM alerts is generative. An agent that detects the alert, isolates the endpoint, opens a ticket and notifies the analyst is agentic. The difference: execution autonomy. That is what changes the game, in both directions.<\/p>\n\n\n\n

Where AI Fits Into the Security Chain<\/h3>\n\n\n\n

Artificial intelligence now permeates nearly the entire cyber stack. SIEMs correlate millions of events using ML. EDRs detect malicious behavior without signatures. SOAR platforms orchestrate response playbooks.<\/p>\n\n\n\n

New entrants add a conversational layer (Microsoft Security Copilot, CrowdStrike Charlotte AI) that turns the analyst into an orchestra conductor. This stacking creates a risk: multiplying AIs without unified governance weakens the overall security posture.<\/p>\n\n\n\n

AI as a Strategic Advantage: Detect, Respond, Anticipate<\/h2>\n\n\n\n

Detect: Behavioral Analytics and Correlation at Scale<\/h3>\n\n\n\n

A corporate SOC receives on average 4,330 alerts per day according to a Ponemon \/ Crogl study published in March 2026. Only 37% of them are actually investigated. The gap between volume and human capacity is unsustainable without AI.<\/p>\n\n\n\n

UEBA (User and Entity Behavior Analytics) builds a behavioral profile for each user and each asset. Any deviation triggers a score. Login at 3 a.m. from a new country, abnormal data exfiltration volume, unusual privilege escalation: these weak signals become actionable.<\/p>\n\n\n\n

Behavioral analytics drastically reduces noise. According to the Gartner Hype Cycle for Security Operations 2025, AI SOC agents remain at the Innovation Trigger stage with 1 to 5% market adoption. Targeted early deployments (alert enrichment, investigation summarization) nonetheless show significant productivity gains.<\/p>\n\n\n\n

Respond: Semi-Automated Remediation and Augmented Playbooks<\/h3>\n\n\n\n

Detection is worthless without rapid response. This is where AI cybersecurity delivers its second leverage effect.<\/p>\n\n\n\n

SOAR platforms already executed hard-coded playbooks. AI brings two new capabilities. First, suggesting the most relevant playbook for a novel incident. Second, dynamically generating remediation steps from context described in natural language.<\/p>\n\n\n\n

In practice: the compromised endpoint is isolated. The malicious file hash is pushed to a blocklist across the fleet. Exposed accounts are forced to reset. A pre-drafted report lands on the CISO’s desk. Mean time to remediation drops from several hours to a few minutes.<\/p>\n\n\n\n

Anticipate: Predictive Threat Intelligence and Attack Simulation<\/h3>\n\n\n\n

The third pillar of AI cybersecurity use cases concerns anticipation. ML engines cross-reference threat intelligence feeds, CVE vulnerabilities and the company’s actual exposure to prioritize patches.<\/p>\n\n\n\n

Adversarial AI<\/strong> tools simulate attacks at scale to test the resilience of an information system. This approach, inherited from pentesting, is industrializing with tools like Pentera or AttackIQ. It complements but does not replace human penetration testing, whose creativity remains unmatched. Castelis covered this in its dedicated pentest article<\/a>.<\/p>\n\n\n\n

Concrete Use Case: An AI-Augmented Managed SOC<\/h3>\n\n\n\n

In the field, AI does not replace the SOC: it redefines the cost-to-coverage ratio. An AI-augmented managed SOC lets a mid-sized company access 24\/7 monitoring without hiring a team of eight analysts.<\/p>\n\n\n\n

Tier 1 tasks (triage and qualification) are largely automated. Human analysts focus on Tier 2 and 3 work: deep investigation, threat hunting, complex incident management. Castelis documented this model in its comparison of in-house vs. managed SOC<\/a>.<\/p>\n\n\n\n

AI as a Threat: The New Generation of Attacks<\/h2>\n\n\n\n

Industrial-Scale Phishing and Deepfakes<\/h3>\n\n\n\n

The first concrete impact of generative AI on the threat landscape concerns phishing. Fraudulent emails written in perfect English, tailored to the target’s industry, signed by a real executive, have become the norm. Email security AI, which detects these AI-generated messages, must evolve as fast as attack techniques.<\/p>\n\n\n\n

Audio and video deepfakes open a second front. The Arup case, disclosed in May 2024, marked a turning point. An employee in the Hong Kong office wired $25.6 million in 15 transactions after a rigged video conference. The CFO and several colleagues attending were in fact deepfakes, generated from publicly available videos. The classic CEO fraud, fifteen years old, becomes undetectable to both ear and eye.<\/p>\n\n\n\n

AI security awareness must therefore evolve. Classic phishing exercises lose relevance: visual markers (typos, suspicious headers) disappear. The new line of defense relies on secondary channel verification and counter-validation procedures.<\/p>\n\n\n\n

Polymorphic Malware and AI Evasion<\/h3>\n\n\n\n

Traditional malware was recognized by its signature. AI-generated malware mutates with each infection. Every sample is unique, bypassing hash-based detection and time-limited sandboxes.<\/p>\n\n\n\n

More worrying: offensive agents are starting to adapt the payload to the target context in real time. If the compromised machine is a user workstation, ransomware deploys. If it is a critical server, the attacker favors quiet persistence and exfiltration.<\/p>\n\n\n\n

Offensive Autonomous Agents: The 2026 Rupture<\/h3>\n\n\n\n

This is the blind spot of classic cybersecurity analyses. Until 2024, a sophisticated attack required expert operator hours. In 2026, open source frameworks make it possible to delegate reconnaissance, exploitation and lateral movement to chained AI agents.<\/p>\n\n\n\n

Agentic AI cybersecurity on the offensive side changes two parameters. First, speed: what took days now executes in hours. Second, scale: a single actor can run dozens of campaigns against dozens of targets in parallel. The marginal cost of an attack trends toward zero. Castelis documented this dynamic on the defensive side through its experience report on OpenClaw, its multi-agent AI framework<\/a>. The same mechanisms, reversed, fuel the offense.<\/p>\n\n\n\n

Poisoning and Attacks Against the Models Themselves<\/h3>\n\n\n\n

The last vector directly targets defensive AIs. Three techniques are emerging beyond academic research. Data poisoning injects biased examples into training datasets to skew future detections.<\/p>\n\n\n\n

Prompt injection hijacks the behavior of an LLM embedded in a business tool (Copilot, SOC agent, HR chatbot) to make it perform unintended actions. Adversarial evasion crafts inputs specifically designed to slip under an ML classifier’s radar.<\/p>\n\n\n\n

Every defensive AI becomes a new attack surface. The paradox is complete.<\/p>\n\n\n\n

The Paradox of Agentic AI in Cybersecurity<\/h2>\n\n\n\n

Why Agentic AI Changes the Balance of Power<\/h3>\n\n\n\n

As long as attack and defense remained human, the limiting factor was reaction time. Agentic AI compresses that time on both sides, but not symmetrically.<\/p>\n\n\n\n

The attacker has a structural advantage: they choose their moment, angle and payload. The defender must cover the entire perimeter, continuously. When both camps have autonomous agents, this asymmetry widens. Unless defense industrializes at the same pace.<\/p>\n\n\n\n

From SOC to AgentOps: Toward Autonomous Defenses<\/h3>\n\n\n\n

Tomorrow’s SOC will not be a classic operations room with an AI copilot. It will be a supervised agent infrastructure, where the human becomes architect and arbiter rather than executor.<\/p>\n\n\n\n

This emerging discipline has a name: AgentOps. It borrows DevOps’ obsession with toolchains, and SRE’s rigor on SLOs. It raises new questions: how do you version an agent? How do you audit a decision made by chained LLMs? How do you guarantee reproducible behavior?<\/p>\n\n\n\n

These questions are not theoretical. They condition the real ability of mid-sized companies to deploy robust AI cybersecurity rather than cosmetic assistants.<\/p>\n\n\n\n

The “Human-out-of-the-Loop” Risk<\/h3>\n\n\n\n

The faster the agent decides, the less time humans have to supervise. At that pace, human supervision becomes symbolic: people sign off on the agent’s decisions without being able to contest them in real time.<\/p>\n\n\n\n

Three safeguards are essential. First: precisely define which actions the agent can take alone, and which require validation. Second: log all decisions exhaustively for post-incident audit. Third: provide a kill switch capable of cutting autonomy at any time.<\/p>\n\n\n\n

Legal liability in case of incident remains an uncertain terrain. The European AI Act sets a framework, but the first case law on autonomous agents is still to be built.<\/p>\n\n\n\n

How to Integrate AI Into Your Cyber Strategy Without Exposing Yourself<\/h2>\n\n\n\n

Map AI Usage (Shadow AI) Before Anything Else<\/h3>\n\n\n\n

Before deploying a defensive AI, you must understand which AIs are already running in your organization. Shadow AI (ungoverned use of ChatGPT, Claude, Gemini, Mistral and the like by employees) has been exploding since 2023.<\/p>\n\n\n\n

According to the IBM Cost of a Data Breach Report 2025, 20% of organizations have already suffered a breach related to shadow AI. The average extra cost reaches $670,000 per incident. More worrying: 97% of organizations victims of an AI-related breach had no proper access controls on their AI tools. Yet every prompt sent can contain confidential data, code snippets, customer information.<\/p>\n\n\n\n

The first action is therefore not to buy a defensive AI. It is to inventory existing AI usage, decide which ones should be framed, allowed or blocked, and propose governed alternatives to the teams.<\/p>\n\n\n\n

Choose Between Embedded Vendor AI, Managed AI and Sovereign AI<\/h3>\n\n\n\n

Three models structure the AI cybersecurity market today.<\/p>\n\n\n\n

Model<\/th>Example<\/th>Advantage<\/th>Limitation<\/th><\/tr><\/thead>
Embedded vendor AI<\/td>Microsoft Security Copilot, CrowdStrike Charlotte AI<\/td>Native integration with the security tool<\/td>Vendor dependency, data sent to the vendor’s cloud<\/td><\/tr>
Partner-managed AI<\/td>AI-augmented managed SOC<\/td>No in-house investment, shared expertise<\/td>Trust in the partner is critical<\/td><\/tr>
Sovereign on-premise AI<\/td>Self-hosted Mistral, open source models<\/td>Data control, NIS2 and regulated sector compliance<\/td>Significant HR and infrastructure cost<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n

The choice depends on the sector, the criticality of the data and the maturity of the security team.<\/p>\n\n\n\n

Governance: Policies, Audits, Human Supervision<\/h3>\n\n\n\n

AI cybersecurity integration must rely on formal governance. An AI policy defines authorized uses, the data that can be submitted to a model, allowed models and forbidden models.<\/p>\n\n\n\n

Regular audits verify model drift, detection quality, and actual usage by teams. Without audits, an AI log analysis tool may silently miss new threats because its dataset is no longer up to date.<\/p>\n\n\n\n

Human supervision is not a formality. It conditions AI Act compliance for high-risk use cases, and is the only rampart against cascading failures.<\/p>\n\n\n\n

Skills to Internalize or Outsource<\/h3>\n\n\n\n

A CISO at a mid-sized company cannot cover everything. Three skills become critical for AI cybersecurity:<\/p>\n\n\n\n

    \n
  • an AI referent able to arbitrate tool choices;<\/li>\n\n\n\n
  • an analyst familiar with ML models, able to challenge detections;<\/li>\n\n\n\n
  • an agent chain architect if the company deploys agentic AI.<\/li>\n<\/ul>\n\n\n\n

    These profiles are rare and expensive. Outsourcing to a specialized partner remains, for most mid-sized companies, a pragmatic choice, provided in-house control over policies and decisions is preserved.<\/p>\n\n\n\n

    Key Takeaways<\/h2>\n\n\n\n

    AI cybersecurity is neither a savior nor a destroyer. It is a multiplier that amplifies existing capabilities on both sides of the front.<\/p>\n\n\n\n

    For a mid-sized company in 2026, the pragmatic path comes down to four points. First, map AI usage already in place before any defensive deployment. Second, industrialize detection and response by relying on partners able to absorb operational complexity. Anticipate the agentic shift on the attacker side, which changes the scale and speed of campaigns. Finally, govern human supervision to avoid drifting toward Human-out-of-the-Loop.<\/p>\n\n\n\n

    Castelis supports CISOs and CIOs of mid-sized companies on these challenges: AI security posture diagnostics, deployment of AI-augmented managed SOCs, production supervision of AI systems in AgentOps mode. Where many players stop at the prototype, Castelis designs, secures and operates production-grade solutions.<\/p>\n\n\n\n

    Frequently Asked Questions<\/h2>\n\n\n\n

    Can AI Replace a SOC Analyst?<\/h3>\n\n\n\n

    No. AI automates the bulk of Tier 1 tasks (triage, qualification, first diagnosis) but Tiers 2 and 3 remain human. Threat hunting, complex incident investigation and decision-making under pressure require creativity and contextual intuition that current models cannot match. AI shifts the analyst’s role toward higher-value tasks.<\/p>\n\n\n\n

    What Are the Main Cyber Risks Tied to Generative AI?<\/h3>\n\n\n\n

    Four risks dominate. Industrialized phishing and deepfakes that bypass classic defenses. On-the-fly polymorphic malware. Prompt injection on internal tools embedding an LLM. Involuntary data exfiltration through ungoverned usage (shadow AI). Each calls for a distinct response combining technology, process and training.<\/p>\n\n\n\n

    What Is an Offensive AI Agent?<\/h3>\n\n\n\n

    An offensive AI agent is an autonomous program able to chain the steps of a cyber attack without continuous human intervention. It can perform target reconnaissance, identify vulnerabilities, exploit a flaw, move laterally and exfiltrate data. Open source frameworks make these capabilities accessible to less-equipped malicious actors than before, increasing both speed and scale of attacks.<\/p>\n\n\n\n

    How Do European Regulators Frame AI in Cybersecurity?<\/h3>\n\n\n\n

    Since 2023, ENISA has published specific recommendations for AI systems, particularly on machine learning model security and the integration of generative AI into information systems. These publications complement the European regulatory framework (AI Act, NIS2) and primarily target operators of essential services. For mid-sized companies, they constitute a reference to be integrated into internal AI policy.<\/p>\n\n\n\n

    Does a Mid-Sized Company Need a Dedicated Defensive AI?<\/h3>\n\n\n\n

    Not necessarily in-house. For most mid-sized companies, access to high-performance AI cybersecurity goes through a managed SOC. Or through existing tools (EDR, SIEM, email security) that already embed AI components. The decisive criterion is not technology ownership but usage governance. A well-governed pooled AI is better than a poorly supervised proprietary one.<\/p>\n\n","protected":false},"excerpt":{"rendered":"

    Detection, response, anticipation: how AI and autonomous agents are reshaping cybersecurity for mid-sized companies in 2026. Risks and opportunities.<\/p>\n","protected":false},"author":2,"featured_media":3026,"template":"","meta":{"_acf_changed":false,"content-type":"","footnotes":""},"categories":[78,77],"tags":[85,94,96,84,95],"class_list":["post-3027","article","type-article","status-publish","has-post-thumbnail","hentry","category-artificial-intelligence","category-ia","tag-agents-ia","tag-cybersecurite","tag-eti","tag-ia-generative","tag-soc"],"acf":[],"_links":{"self":[{"href":"https:\/\/www.castelis.com\/en\/wp-json\/wp\/v2\/article\/3027","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.castelis.com\/en\/wp-json\/wp\/v2\/article"}],"about":[{"href":"https:\/\/www.castelis.com\/en\/wp-json\/wp\/v2\/types\/article"}],"author":[{"embeddable":true,"href":"https:\/\/www.castelis.com\/en\/wp-json\/wp\/v2\/users\/2"}],"version-history":[{"count":1,"href":"https:\/\/www.castelis.com\/en\/wp-json\/wp\/v2\/article\/3027\/revisions"}],"predecessor-version":[{"id":3029,"href":"https:\/\/www.castelis.com\/en\/wp-json\/wp\/v2\/article\/3027\/revisions\/3029"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.castelis.com\/en\/wp-json\/wp\/v2\/media\/3026"}],"wp:attachment":[{"href":"https:\/\/www.castelis.com\/en\/wp-json\/wp\/v2\/media?parent=3027"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.castelis.com\/en\/wp-json\/wp\/v2\/categories?post=3027"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.castelis.com\/en\/wp-json\/wp\/v2\/tags?post=3027"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}