Author: Raj Date: February 2026 Keywords: Agentic AI, Multi-Agent Systems, Enterprise Security, SOC Automation, SAST, DAST, DevSecOps, Threat Detection, Vulnerability Management, Security Orchestration
Enterprise security is undergoing a fundamental transformation driven by the emergence of agentic AI—autonomous, goal-directed AI systems capable of reasoning, planning, executing, and adapting without continuous human oversight. This paper examines how multi-agent architectures are poised to revolutionize enterprise security operations by enabling end-to-end automation across threat detection, vulnerability management, incident response, application security testing, and compliance governance. We analyze the current state of agentic AI adoption in security, evaluate emerging multi-agent orchestration frameworks, assess real-world deployments by major platform vendors, and identify the risks, governance challenges, and architectural patterns that will define the next generation of enterprise security posture. We argue that organizations adopting coordinated multi-agent security systems will achieve a decisive advantage in speed, scalability, and resilience against increasingly sophisticated adversaries—including adversaries themselves leveraging agentic AI.
Enterprise security teams face an untenable situation. The average Security Operations Center (SOC) analyst processes thousands of alerts daily, with false positive rates exceeding 40% in many organizations. Mean time to detect (MTTD) breaches remains measured in days or weeks, not minutes. The cybersecurity talent shortage—estimated at 3.5 million unfilled positions globally—shows no sign of abating. Meanwhile, attack surfaces have expanded exponentially: cloud-native architectures, microservices, APIs, CI/CD pipelines, third-party integrations, and now AI systems themselves all present new vectors.
Traditional automation approaches—SOAR playbooks, rule-based SIEM correlation, static vulnerability scanners—have reached their ceiling. These tools execute predefined logic against known patterns. They cannot reason about novel attack chains, adapt to evolving adversary tactics, or make contextual decisions across the full security lifecycle.
Agentic AI represents a qualitative leap beyond both traditional automation and conversational AI assistants. An AI agent is a system that can:
- Perceive its environment through tool integrations and data ingestion
- Reason about goals, constraints, and context
- Plan multi-step action sequences
- Execute actions through tool use and API calls
- Observe outcomes and adapt its approach
- Learn from feedback loops to improve over time
Unlike a chatbot that responds to queries or a playbook that executes fixed steps, an agentic system operates in a continuous perceive-reason-act loop, autonomously pursuing objectives while handling ambiguity and novel situations.
The market trajectory reflects this shift. The global agentic AI market is projected to grow from $5.25 billion in 2024 to $199.05 billion by 2034, representing a CAGR of approximately 43.8%. Gartner reported a 1,445% surge in multi-agent system inquiries from Q1 2024 to Q2 2025. By 2026, 52% of executives in AI-using organizations have AI agents in production, with 46% of those specifically adopting agents for security operations and cybersecurity.
This paper argues that multi-agent AI systems will become the dominant architecture for enterprise security within the next three to five years, replacing siloed point-solution automation with coordinated, adaptive, and scalable security operations. We present a reference architecture, examine current vendor implementations, analyze the security-of-security challenges this introduces, and propose governance frameworks for responsible deployment.
The first generation of security automation relied on static rules, signatures, and deterministic playbooks. SIEM platforms correlated log events against predefined detection rules. SOAR tools automated incident response through sequential, hardcoded workflows. Vulnerability scanners matched known CVEs against asset inventories.
Limitations:
- Brittle: any deviation from expected patterns caused failures
- High false positive rates without contextual understanding
- Unable to handle novel or chained attack techniques
- Required extensive manual playbook engineering and maintenance
- No capacity for reasoning or adaptation
The second generation introduced machine learning models for anomaly detection, natural language processing for log analysis, and generative AI copilots that could summarize incidents and suggest responses. Tools like Microsoft Security Copilot and Google's Gemini-powered security workbench augmented human analysts.
Improvements:
- Reduced alert fatigue through ML-based prioritization
- Natural language interfaces lowered skill barriers
- Pattern recognition across larger datasets
- Faster initial triage
Remaining Limitations:
- Reactive, not proactive—still required human-in-the-loop for decisions
- Single-model, single-task: each tool operated independently
- No autonomous multi-step reasoning or execution
- Could suggest actions but not carry them out end-to-end
The current generation deploys autonomous AI agents that can independently investigate incidents, hunt for threats, remediate vulnerabilities, and orchestrate responses across the security stack. These agents operate in continuous loops, maintain memory and context across interactions, use tools and APIs as first-class capabilities, and coordinate with other agents and human operators.
Key characteristics:
- Autonomous goal pursuit with minimal human intervention
- Multi-step reasoning and planning
- Tool use across heterogeneous security platforms
- Persistent memory and learning from past incidents
- Human-in-the-loop at critical decision points, not every step
- Multi-agent coordination for complex, cross-domain tasks
This is not an incremental improvement—it is an architectural paradigm shift.
A single monolithic agent attempting to handle all security functions would face fundamental scaling, specialization, and reliability challenges:
- Cognitive overload: No single model can maintain deep expertise across threat intelligence, code security, cloud configuration, network forensics, compliance, and incident response simultaneously.
- Blast radius: A compromised or malfunctioning single agent has access to everything.
- Latency: Sequential processing of diverse tasks creates bottlenecks.
- Maintainability: Updating one capability requires redeploying the entire system.
Multi-agent architectures address these by distributing capabilities across specialized agents that communicate through structured protocols, share context through common memory systems, and are governed by coordination layers.
We propose the following reference architecture for enterprise multi-agent security systems:
┌─────────────────────────────────────────────────────────────────┐
│ HUMAN OVERSIGHT LAYER │
│ (CISO Dashboard / Approval Gates / Audit Logs) │
└────────────────────────────┬────────────────────────────────────┘
│
┌────────────────────────────▼────────────────────────────────────┐
│ ORCHESTRATION AGENT (Coordinator) │
│ ┌──────────┐ ┌──────────┐ ┌──────────┐ ┌──────────┐ │
│ │ Task │ │ Priority │ │ Resource │ │ Conflict │ │
│ │ Routing │ │ Engine │ │ Manager │ │ Resolver │ │
│ └──────────┘ └──────────┘ └──────────┘ └──────────┘ │
└──┬──────┬──────┬──────┬──────┬──────┬──────┬──────┬────────────┘
│ │ │ │ │ │ │ │
▼ ▼ ▼ ▼ ▼ ▼ ▼ ▼
┌──────┐┌──────┐┌──────┐┌──────┐┌──────┐┌──────┐┌──────┐┌──────┐
│Threat││Vuln ││AppSec││Cloud ││ IR ││Comp- ││Threat││ IAM │
│Detect││Mgmt ││Agent ││Config││Agent ││liance││Intel ││Agent │
│Agent ││Agent ││ ││Agent ││ ││Agent ││Agent ││ │
└──┬───┘└──┬───┘└──┬───┘└──┬───┘└──┬───┘└──┬───┘└──┬───┘└──┬───┘
│ │ │ │ │ │ │ │
▼ ▼ ▼ ▼ ▼ ▼ ▼ ▼
┌─────────────────────────────────────────────────────────────────┐
│ SHARED CONTEXT LAYER │
│ ┌────────────┐ ┌────────────┐ ┌────────────┐ │
│ │ Vector DB │ │ Knowledge │ │ Incident │ │
│ │ (Embeddings│ │ Graph │ │ Memory │ │
│ │ & Context)│ │ │ │ Store │ │
│ └────────────┘ └────────────┘ └────────────┘ │
└─────────────────────────────────────────────────────────────────┘
│
┌────────────────────────────▼────────────────────────────────────┐
│ TOOL INTEGRATION LAYER │
│ SIEM │ EDR │ CSPM │ SAST │ DAST │ SCA │ CI/CD │ Cloud APIs │
└─────────────────────────────────────────────────────────────────┘
- Continuously monitors SIEM, EDR, and network telemetry
- Correlates anomalies across data sources using reasoning, not just rules
- Generates hypotheses about potential attack chains
- Escalates to the IR Agent when confidence thresholds are met
- Adapts detection logic based on emerging threat intelligence
- Orchestrates continuous scanning across infrastructure, applications, and containers
- Contextualizes CVEs against actual exposure: Is this reachable? Is it exploitable? Is the asset internet-facing?
- Prioritizes remediation based on real-world risk, not just CVSS scores
- Generates and validates patches, coordinating with CI/CD pipelines
- Tracks remediation SLAs and escalates non-compliance
- Integrates into CI/CD pipelines to perform SAST and DAST at commit and deployment stages
- Reviews AI-generated code for security vulnerabilities in real time
- Coordinates with SCA agents for dependency risk analysis
- Provides contextual remediation guidance directly to developers
- Maintains a continuously updated threat model per application
- Monitors cloud environments (AWS, Azure, GCP) for misconfigurations, drift, and policy violations
- Enforces infrastructure-as-code security baselines
- Detects and remediates overly permissive IAM policies
- Maps blast radius of configuration changes
- Coordinates with the Compliance Agent for regulatory alignment
- Receives escalations from detection and threat intelligence agents
- Autonomously conducts initial investigation: timeline reconstruction, IOC extraction, scope assessment
- Executes containment actions (isolate host, revoke credentials, block IPs) within authorized boundaries
- Generates incident reports with evidence chains
- Manages post-incident review workflows
- Continuously maps security controls to regulatory frameworks (SOC 2, ISO 27001, PCI DSS, HIPAA, GDPR)
- Identifies compliance gaps in real time, not quarterly
- Generates audit-ready evidence packages automatically
- Monitors policy adherence across all other agents' actions
- Flags actions that would create regulatory exposure before they execute
- Ingests and correlates intelligence from OSINT, commercial feeds, dark web monitoring, and internal telemetry
- Enriches alerts and investigations with adversary TTPs (MITRE ATT&CK mapping)
- Proactively identifies threats targeting the organization's industry and technology stack
- Distributes actionable intelligence to other agents in structured formats
- Monitors for anomalous authentication patterns and credential abuse
- Enforces least-privilege principles through continuous access reviews
- Detects and responds to identity-based attacks (credential stuffing, token theft, session hijacking)
- Manages automated provisioning and deprovisioning workflows
Effective multi-agent security requires robust communication protocols. Two standards are emerging:
Model Context Protocol (MCP): An open standard for connecting AI agents to tools, data sources, and each other. MCP enables agents to share context, invoke tools across platforms, and maintain consistent state.
Agent-to-Agent Protocol (A2A): Enables structured communication between agents for task delegation, status updates, conflict resolution, and collaborative reasoning.
These protocols are critical for preventing the "swivel-chair" anti-pattern where agents operate in isolation, duplicating work or making conflicting decisions.
CrowdStrike's Falcon platform represents the most mature enterprise agentic SOC implementation. Charlotte AI provides:
- Autonomous alert triage and investigation across endpoints, cloud, and identity
- Natural language agent creation through AgentWorks, enabling analysts to build custom agents without code
- Integration with the Falcon platform's unified data model across EDR, XDR, cloud security, and identity protection
- CrowdStrike's ARR surpassed $5.2 billion by end of 2025, with 73% year-over-year growth in net new ARR driven by platform consolidation
Palo Alto Networks is replacing Cortex XSOAR with Cortex AgentiX, marking a generational shift from rule-based SOAR to agentic orchestration:
- AI agents that reason about incidents rather than executing fixed playbooks
- Deep integration with Cortex XDR and Prisma Cloud
- Strategic partnership with Google Cloud for secure agentic AI deployment across hybrid multi-cloud environments
- Predicted to be generally available in early 2026
Google integrated Chronicle SIEM and Mandiant threat intelligence into a unified security operations platform powered by Gemini:
- Leverages Google's search infrastructure for security data analysis at scale
- Mandiant's threat intelligence provides real-world adversary context
- Gemini-powered agents for autonomous investigation and threat hunting
- Cloud-native architecture enabling elastic scaling
Microsoft Security Copilot has evolved from an assistant to an agentic platform:
- Agents that autonomously investigate incidents across the Microsoft 365 and Azure ecosystem
- Integration with Defender XDR, Sentinel, and Intune
- Reference to OWASP's agentic threat and mitigation frameworks in their security design patterns
- Embedded in the world's largest enterprise productivity ecosystem
OpenAI's Aardvark represents a specialized application of agentic AI to security research:
- Autonomous vulnerability discovery in software systems
- Validates findings and provides remediation guidance
- Designed to operate at scale across large codebases
- Signals the convergence of AI capabilities and security expertise
The most transformative near-term impact of agentic AI in enterprise security is in application security, where agents are fundamentally changing the economics of secure software development.
Traditional SAST tools generate thousands of findings, many of which are false positives or low-priority issues buried in unused code paths. Agentic SAST fundamentally reimagines this:
- Context-aware scanning: Agents understand application architecture, data flows, and business logic—not just syntax patterns. They trace user input from entry points to sinks across files, modules, and services.
- AI code generation security: As enterprises adopt AI coding assistants (Cursor, GitHub Copilot, Claude Code), agentic SAST tools integrate via MCP servers to check AI-generated code for vulnerabilities in real time. The agent can iterate up to three times to produce secure code before it ever reaches the repository.
- Prioritization by exploitability: Instead of reporting every potential SQL injection, the agent assesses: Is the input actually user-controllable? Is there parameterized query use upstream? Is the endpoint authenticated? It reports only genuinely exploitable issues.
- Automated remediation: Agents don't just find vulnerabilities—they generate fixes, validate that fixes don't break functionality, and submit pull requests.
Dynamic testing is being transformed by agents that can:
- Navigate complex application flows (multi-step authentication, stateful transactions)
- Adapt scanning strategies based on application responses and emerging attack patterns
- Correlate runtime findings with SAST results, SCA data, and infrastructure context
- Prioritize findings by actual exploitability in the running environment
- Conduct intelligent fuzzing that evolves based on application behavior
The 2026 landscape has seen the emergence of agentic red teaming—AI agents that autonomously:
- Conduct reconnaissance and attack surface mapping
- Identify and chain vulnerabilities across multiple vectors
- Execute multi-stage exploitation paths
- Adapt tactics based on defensive responses
- Generate comprehensive penetration test reports with evidence
This does not replace human penetration testers but provides continuous, scalable testing between manual engagements.
The SOC is where agentic AI delivers the most immediate operational impact. The agentic SOC model operates as a semi-autonomous cycle:
- Alert ingestion: Agents continuously consume alerts from SIEM, EDR, NDR, and cloud security tools
- Automated triage: Agents correlate, deduplicate, and prioritize alerts using contextual reasoning—resolving up to 95% of Tier-1 alerts and many Tier-2 tasks without human involvement
- Investigation: Agents autonomously investigate by querying additional data sources, enriching with threat intelligence, reconstructing attack timelines, and assessing scope
- Response: Within authorized boundaries, agents execute containment and remediation actions
- Escalation: Complex or high-impact incidents are escalated to human analysts with full investigation context
- Learning: Each incident feeds back into detection logic, reducing future false positives and improving response playbooks
Industry data suggests this model automates 90% of incident investigation and response tasks, functioning as an always-on security operations team.
Beyond detection and response, multi-agent systems enable continuous security posture management:
- Attack surface management: Agents continuously discover and inventory external-facing assets, identifying shadow IT, forgotten services, and exposure changes
- Configuration drift detection: Cloud and infrastructure agents detect when configurations deviate from security baselines and either alert or auto-remediate
- Patch management orchestration: Vulnerability agents coordinate with IT operations to schedule, test, and deploy patches based on risk priority and change windows
- Security control validation: Agents continuously test that security controls (WAF rules, network segmentation, access controls) function as intended
The same capabilities that make agentic AI transformative for defenders are being weaponized by adversaries:
- Autonomous attack chains: Multi-agent systems using reinforcement learning can autonomously plan, adapt, and execute entire attack lifecycles from reconnaissance through exfiltration, continuously adjusting based on real-time feedback.
- AI-powered social engineering: Deepfake-based impersonation, AI-generated phishing at scale, and adaptive pretexting that evolves based on target responses.
- Autonomous malware: Malware that uses agentic capabilities to evade detection, adapt to defenses, and propagate intelligently.
- AI supply chain attacks: Gartner predicts AI supply chain attacks will become a top-five attack vector by 2026, targeting model weights, training data, and agent tool chains.
This creates an asymmetric arms race where:
- Defenders must secure complex, multi-agent systems while attackers only need to find one weakness
- Attack agents can operate without ethical constraints, governance overhead, or approval gates
- The speed of AI-powered attacks compresses response windows from hours to seconds
- Organizations without agentic defenses will face agentic attacks with manual processes
This dynamic makes agentic security adoption not optional but existential for enterprises.
The OWASP GenAI Security Project, with contributions from over 100 industry experts, has identified the critical risks facing agentic AI systems:
- Prompt Injection and Goal Manipulation: Attackers use prompt injection, poisoned data, and adversarial inputs to manipulate agent goals—for example, causing a financial agent to transfer funds to attacker-controlled accounts.
- Tool Misuse: Agents misuse legitimate, authorized tools for data exfiltration, destructive actions, or lateral movement. Real-world examples include AI agents deleting databases and wiping hard drives.
- Privilege Escalation: Flaws in agent identity, delegation, or privilege inheritance enable attackers to escalate access, exploit confused deputy scenarios, or execute unauthorized cross-system actions.
- Memory Poisoning: Corrupting agent memory stores to influence future decisions and actions.
- Cascading Failures: In multi-agent systems, a compromised or malfunctioning agent can trigger chain reactions across the agent network.
- Data Leakage Through Agent Context: Agents accumulating sensitive data in context windows or shared memory systems create new exfiltration vectors.
- Supply Chain Compromise: Attacks targeting agent frameworks, tool integrations, MCP servers, or model weights.
Defending agentic AI systems requires a defense-in-depth approach:
- Least-privilege tool access: Each agent should have access only to the tools and data sources required for its specific role, with no shared superuser credentials.
- Action authorization gates: Critical actions (host isolation, credential revocation, production deployments) require human approval or multi-agent consensus.
- Agent identity and authentication: Each agent operates with its own identity, auditable credentials, and defined trust boundaries.
- Input validation and sanitization: All data flowing between agents and from external sources must be validated to prevent injection attacks.
- Memory integrity: Agent memory stores must be protected against tampering, with integrity verification and anomaly detection.
- Audit logging: Every agent action, decision, and inter-agent communication must be logged immutably for forensic analysis and compliance.
- Kill switches and circuit breakers: The ability to immediately halt any agent or the entire multi-agent system in case of detected compromise or malfunction.
- Adversarial testing: Regular red-teaming of agent systems, including prompt injection testing, tool misuse scenarios, and multi-agent failure mode analysis.
When agents make decisions, call tools, and handle sensitive data without continuous human oversight, traditional security governance models break down. Boards and regulators are asking: How do organizations prove control over autonomous systems that can access sensitive data, invoke tools, and trigger automated workflows?
Current data shows that 87% of organizations use human-supervised agents, with 69% of agentic AI decisions verified by humans. However, as agent capabilities increase and trust is established, the ratio of autonomous to supervised actions will shift—requiring governance frameworks that scale with autonomy.
We propose a tiered governance model:
Tier 1 — Full Autonomy (Low Risk)
- Alert triage and prioritization
- Log analysis and correlation
- Vulnerability scanning and assessment
- Threat intelligence enrichment
- Report generation
Tier 2 — Supervised Autonomy (Medium Risk)
- Automated containment actions (with audit trail)
- Patch deployment to non-critical systems
- Access policy modifications
- Configuration remediation
- Code security review with developer notification
Tier 3 — Human-Approved (High Risk)
- Production system isolation
- Credential revocation for privileged accounts
- Firewall rule changes affecting production traffic
- Data destruction or quarantine
- Incident escalation to external parties (law enforcement, regulators)
Tier 4 — Human-Only (Critical)
- Strategic security architecture decisions
- Vendor and tool selection
- Policy and standard creation
- Legal and regulatory response
- Public communications about security incidents
The adoption of multi-agent security systems will reshape security organizations:
- SOC analyst roles evolve: From alert processors to agent supervisors, exception handlers, and AI trainers. The analyst's job shifts from "investigate this alert" to "validate this agent's investigation" and "improve this agent's detection logic."
- Security engineering becomes agent engineering: Building, configuring, testing, and maintaining security agents becomes a core competency alongside traditional security engineering.
- New roles emerge: Agent Security Architect, AI Red Team Lead, Agent Governance Officer, Multi-Agent System Reliability Engineer.
- Cross-functional integration deepens: As agents span security, development, operations, and compliance, organizational silos become untenable.
- Assess current security tooling and identify integration points
- Select an agentic security platform (or build on open frameworks)
- Deploy initial agents for low-risk, high-volume tasks (alert triage, log analysis)
- Establish governance framework and approval workflows
- Build monitoring and audit infrastructure for agent actions
- Train security team on agent supervision and management
- Deploy specialized agents across the security lifecycle (AppSec, cloud, IAM, compliance)
- Implement inter-agent communication via MCP/A2A protocols
- Integrate agents into CI/CD pipelines for shift-left security
- Establish shared context layer (vector DB, knowledge graph, incident memory)
- Conduct adversarial testing of agent systems
- Measure and optimize: MTTD, MTTR, false positive rates, coverage metrics
- Enable multi-agent orchestration for complex, cross-domain security operations
- Expand autonomous action authority based on established trust and track record
- Implement continuous agent improvement through feedback loops
- Integrate with business risk management and executive reporting
- Contribute to industry standards and frameworks
- Achieve continuous, adaptive security posture management
- Full agentic security mesh operating across all domains
- Predictive security: agents anticipating threats before they materialize
- Self-healing infrastructure: automated remediation at machine speed
- Real-time compliance: continuous regulatory adherence without manual audits
- Security as competitive advantage: faster, safer product delivery
- Hallucination and reliability: LLM-based agents can generate incorrect analysis or take wrong actions. Formal verification and output validation remain active research areas.
- Latency at scale: Real-time security decisions require sub-second agent reasoning, which current LLM inference speeds may not consistently deliver.
- Context window limitations: Complex investigations may exceed model context windows, requiring sophisticated memory management.
- Tool integration fragmentation: The security tool ecosystem lacks standardized APIs, making agent-tool integration brittle.
- Evaluation and benchmarking: No standardized benchmarks exist for measuring agentic security system effectiveness.
- Trust calibration: Organizations must learn when to trust agent decisions and when to override—a calibration that takes time and experience.
- Skill gap: Security teams need new competencies in AI/ML, prompt engineering, agent architecture, and AI safety.
- Vendor lock-in: Early agentic platforms may create deep dependencies that are difficult to reverse.
- Cost management: LLM inference costs for continuous security operations at enterprise scale are non-trivial.
- Accountability: When an agent makes a wrong decision that causes damage, who is liable—the vendor, the deploying organization, or the agent designer?
- Transparency: Regulatory requirements for explainable AI decisions in security contexts are evolving and unclear.
- Bias: Agents trained on historical security data may inherit biases in threat detection and response prioritization.
- Dual use: The same agent architectures used for defense can be repurposed for attack, raising proliferation concerns.
Future architectures may enable federated agent networks across organizations—sharing threat intelligence, coordinated response actions, and collective defense while maintaining data sovereignty. Industry-specific agent consortiums (financial services, healthcare, critical infrastructure) could provide sector-wide protection.
As reinforcement learning and self-improvement capabilities mature, security agents may continuously evolve their detection logic, response strategies, and tool usage without explicit human retraining—though this raises significant governance and safety challenges.
The emergence of quantum computing threats will require security agents that can autonomously assess cryptographic exposure, plan migration strategies, and implement quantum-resilient alternatives across the enterprise.
Governance frameworks and regulations will need to co-evolve with agentic capabilities. Standards bodies including OWASP, NIST, and ISO are already developing frameworks specifically for agentic AI security. Enterprises should actively participate in shaping these standards rather than waiting for compliance mandates.
Agentic AI is not another incremental improvement in security tooling—it is an architectural paradigm shift that will redefine how enterprises defend themselves. The convergence of large language model capabilities, multi-agent orchestration frameworks, and mature security tool ecosystems has created the conditions for a fundamental transformation in security operations.
Organizations that successfully deploy coordinated multi-agent security systems will achieve:
- Speed: Machine-speed detection and response, compressing MTTD and MTTR from days to minutes
- Scale: Continuous security coverage across expanding attack surfaces without proportional headcount growth
- Adaptability: Defenses that evolve with the threat landscape rather than lagging behind it
- Consistency: Elimination of human fatigue, cognitive bias, and procedural shortcuts in security operations
- Integration: End-to-end security from code commit to production runtime, unified through shared agent context
However, this transformation is not without risk. The security of the agents themselves, the governance of autonomous decision-making, the organizational change management required, and the adversarial AI arms race all represent serious challenges that must be addressed deliberately and proactively.
The enterprises that will thrive are those that begin building agentic security capabilities now—not as science fiction, but as engineering discipline. The tools exist. The architectures are proven. The threat landscape demands it.
The question is no longer whether agentic AI will transform enterprise security. It is whether your organization will be the one deploying agentic defenses—or the one facing agentic attacks with yesterday's tools.
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This paper reflects the state of the field as of February 2026. Given the pace of development in agentic AI, significant advances may occur between publication and reading.