AI Security Operations Center (SOC) Market

Market Overview

The AI Security Operations Center (SOC) market encompasses software platforms and services that integrate machine learning, large language models, generative AI, and autonomous AI agents into the core workflows of enterprise security operations. These technologies enable organizations to automate threat detection, security alert prioritization, incident investigation, threat hunting, and response orchestration, helping security teams manage increasingly complex cyber threats across enterprise networks, cloud environments, and digital infrastructure. The market excludes traditional SIEM deployments without native AI layers, standalone endpoint protection products, and network perimeter hardware not integrated into SOC workflows.

SOC operations sit at the intersection of cybersecurity, enterprise software, and cloud infrastructure, making the AI SOC market directly sensitive to enterprise cloud adoption rates, cybersecurity budget cycles, and the pace of adversary innovation. Security teams that historically operated SIEM and SOAR tools in separate stacks are consolidating those functions into unified AI-native platforms. Vendors that can demonstrate measurable reductions in analyst workload and dwell time now hold a structural pricing advantage over point-solution providers.

Autonomous AI agents are reshaping the investigation workflow faster than most security leaders anticipated. Agents now handle alert triage, phishing classification, malware analysis, and initial containment steps without analyst intervention, compressing the time between detection and response. Buyers are no longer evaluating AI as a feature add-on. Purchasing decisions now center on agent accuracy, governance controls, and safe-response boundaries.

Key Takeaways

• The market size is USD 21.24 Billion in 2026, and is projected to hit USD 153.76 Billion by 2035 at a CAGR of 24.6%.
• By Offering: Software Platforms led as the largest category in 2026.
• By Software Platform: AI-Enabled Detection and Analytics Platforms led as the largest category in 2026.
• By Service: AI-Augmented Managed Detection and Response (MDR) led as the largest category in 2026.
• By Organization Size: Large Enterprises led with a 60.6% share in 2026.
• By Application: Threat Detection and Monitoring led as the largest category in 2026.
• By Vertical: BFSI led as the largest category in 2026.
• By Region: North America led with a 43.2% share, valued at USD 9.17 Billion, in 2026.
• SMEs represent the fastest-growing organization size segment, with a CAGR of 20.1%, as mid-market buyers adopt AI SOC tools previously accessible only to large enterprises.

Market Size and Forecast

The Global AI Security Operations Center (SOC) Market size is estimated at USD 21.24 Billion in 2026, and is projected to reach USD 153.76 Billion by 2035, exhibiting a CAGR of 24.6% during the forecast period.

Enterprise interest in AI-powered security operations is expanding as organizations evaluate automated alert triage, incident investigation, and response orchestration capabilities. Security teams are increasingly exploring AI-native SOC platforms to address rising alert volumes, complex cloud environments, and cybersecurity workforce constraints. However, adoption rates vary according to organizational size, security maturity, budget availability, and regulatory requirements. Pilot deployments and proof-of-concept evaluations indicate growing interest in AI SOC technologies, although conversion into full-scale production deployments depends on demonstrated accuracy, operational reliability, integration capabilities, and measurable security outcomes.

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Historical growth between 2020 and 2024 was propelled by enterprise cloud migrations and the collapse of traditional perimeter security models. Forecast assumptions embed continued adversary automation, sustained enterprise SOC budget growth above overall IT spending, and accelerating displacement of legacy SIEM deployments by AI-native platforms. A downside scenario would emerge if AI governance regulations across the EU or US impose mandatory human-approval gates on autonomous containment actions, slowing agentic deployment timelines in regulated sectors.

Offering Analysis

Software Platforms led the Offering segment as the largest category in 2026.

Software platforms dominate because enterprises want direct control over detection logic, model behavior, and data residency. Buyers deploying AI-native platforms can retrain models on proprietary telemetry, tune alert thresholds, and audit agent decisions within their own environments. Service-dependent models offer less auditability, a constraint that enterprise security teams and regulators increasingly reject.

Services capture the mid-market and SME segments where internal AI engineering capacity is absent. AI-Augmented Managed Detection and Response holds the largest services share, while AI SOC-as-a-Service is the fastest-growing services sub-segment. SOCaaS growth reflects a structural shift: smaller organizations want consumption-based AI analyst capacity without the capital expense of platform licensing, implementation, and ongoing model management.

Software Platform Analysis

AI-Enabled Detection and Analytics Platforms accounted for the largest share of Software Platform demand in 2026, the highest of any category.

Detection and analytics platforms attract the largest share because threat detection remains the primary SOC mandate. Buyers prioritize platforms that ingest multi-source telemetry, correlate signals across endpoints, identities, and cloud workloads, and surface prioritized alerts with evidence chains analysts can verify. Vendors that deliver explainable detections retain contracts. Those producing opaque outputs face replacement.

AI SOC Agent Solutions carry the highest CAGR within the software platform segment. Early deployments focus on discrete tasks, triage and phishing classification, but buyers are extending agent scope to malware analysis, threat hunting, and response orchestration. Security Data Platforms, AI Governance and Risk tools, and Threat Intelligence Platforms each serve distinct buyer personas but are converging toward unified AI-native architectures.

Service Analysis

With the largest share in 2026, AI-Augmented Managed Detection and Response (MDR) outpaced all other Service categories.

MDR services with embedded AI attract buyers who lack the headcount to run 24x7 SOC operations internally. AI augmentation allows MDR providers to cover more endpoints per analyst, accelerate triage, and deliver faster mean-time-to-contain metrics that justify premium pricing. Providers unable to demonstrate AI-derived speed improvements face margin compression as the market commoditizes baseline MDR.

AI SOC-as-a-Service is the fastest-growing service category. SOCaaS packages appeal to buyers who want managed expertise without multi-year platform contracts. Threat Intelligence and Advisory Services and Incident Response and Forensics Services continue to generate revenue but grow more slowly as buyers absorb those functions into AI-platform capabilities.

Organization Size Analysis

Large Enterprises captured 60.6% of the Organization Size segment in 2026, ahead of all rivals.

Large enterprises hold dominant share because complex, multi-cloud environments generate the alert volumes that justify AI SOC investment. Security teams managing thousands of endpoints and dozens of SaaS applications cannot triage manually at scale. AI platforms reduce the analyst-to-alert ratio to a level that makes enterprise security operations sustainable without proportional headcount growth.

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Small and Medium-sized Enterprises (SMEs) are expected to witness significant growth in the AI Security Operations Center (SOC) Market as affordable cloud-based security platforms, managed detection services, and AI-powered automation improve access to advanced cybersecurity capabilities. Increasing exposure to phishing, ransomware, and cloud security threats is encouraging SMEs to adopt automated threat monitoring and incident response solutions. Subscription-based pricing and AI SOC-as-a-Service offerings are further supporting adoption among organizations with limited internal cybersecurity resources.

Application Analysis

Threat Detection and Monitoring led the Application segment as the largest category in 2026.

Threat detection and monitoring anchors SOC workflows and attracts the broadest spend because all subsequent SOC actions depend on detection quality. Buyers allocate the largest application budget to platforms that reduce false-positive rates and surface true positives earlier in the attack chain. Cloud Security Monitoring is expanding rapidly as cloud workload complexity creates new blind spots traditional detection tools cannot cover.

Incident Investigation and Analysis is expected to experience strong growth during the forecast period, supported by increasing adoption of AI-powered investigation tools, automated event correlation, and intelligent security analytics. AI agents can assist security analysts by collecting contextual threat information, identifying suspicious activity patterns, and generating incident timelines, improving investigation efficiency and supporting faster response decisions.

Vertical Analysis

A dominant share made BFSI the clear leader across Vertical categories in 2026.

BFSI organizations face the highest concentration of financially motivated attacks and operate under the most prescriptive compliance regimes. Regulatory bodies in the US, EU, and Asia-Pacific require documented incident timelines and evidence chains, which AI SOC platforms produce automatically. Banks and insurers treating AI SOC as a compliance infrastructure investment, not only a security tool, drive the sector's outsized share.

Energy and Utilities is expected to emerge as an important growth segment, supported by increasing cybersecurity investments across power generation facilities, electricity distribution networks, oil and gas infrastructure, and other critical industrial environments. The adoption of AI-powered security monitoring, operational technology threat detection, and automated incident response capabilities is strengthening cyber resilience across essential infrastructure.

The AI Security Operations Center (SOC) Market Report is segmented on the basis of the following:

By Offering

• Software Platforms
  • AI-Enabled Detection and Analytics Platforms
  • AI SOC Agent Solutions
  • Security Data Platforms
  • AI Governance, Risk and Compliance Solutions
  • AI-Native SOC Platforms
  • Threat Intelligence Platforms
  • AI-Orchestrated Response and Automation Platforms
• Services
  • AI-Augmented Managed Detection and Response (MDR)
  • Threat Intelligence and Advisory Services
  • Incident Response and Forensics Services
  • AI SOC-as-a-Service (SOCaaS)

By Organization Size

• Large Enterprises
• SMEs

By Application

• Threat Detection and Monitoring
• Cloud Security Monitoring
• Security Analytics and Visualization
• Incident Response and Remediation
• Alert Triage and Prioritization
• Identity and Access Monitoring
• Incident Investigation and Analysis
• Threat Hunting
• Compliance Monitoring and Reporting
• Insider Threat Detection

By Vertical

• BFSI
• Manufacturing
• Education
• Energy and Utilities
• Retail and E-commerce
• Government and Defense
• Media and Entertainment
• IT and Telecommunications
• Others
• Healthcare and Life Sciences

Regional Analysis

North America led the AI SOC market with a 43.2% share, valued at USD 9.17 Billion, in 2026.

North America

North America's lead reflects the concentration of large enterprises, federal agencies, and financial institutions that have the telemetry volumes and budget authority to justify agentic SOC platforms. US-based technology vendors dominate the supply side, reducing integration friction for domestic buyers. Federal mandates following high-profile infrastructure attacks accelerated procurement timelines in 2024 and 2025, pulling forward spend that analysts had forecast for later in the decade.

Asia Pacific

Asia Pacific is the fastest-growing region across the forecast period. Rapid cloud adoption among manufacturers, financial services firms, and government agencies in China, India, South Korea, and Australia is generating telemetry volumes that overwhelm legacy security operations. Regulatory tightening on data breach disclosure in India and Singapore is converting compliance pressure into active SOC investment. Domestic vendors in South Korea and Japan are developing region-specific AI SOC models trained on local threat actor behavior.

Europe

Europe represents an important market for AI Security Operations Center solutions, supported by increasing cybersecurity investments, regulatory compliance requirements, and growing adoption of cloud-based security infrastructure. Countries such as Germany, the United Kingdom, France, Spain, and Italy are strengthening enterprise security operations to address ransomware, data breaches, and cyber threats affecting critical infrastructure. Regulations such as the NIS2 Directive and the Digital Operational Resilience Act (DORA) are increasing the importance of incident monitoring, risk management, and cybersecurity reporting. AI-enabled threat detection, security automation, and managed SOC services are gaining attention among financial institutions, manufacturers, telecommunications providers, and government organizations.

Latin America

Latin America is experiencing growing demand for AI-powered security operations as businesses expand digital banking, cloud infrastructure, e-commerce platforms, and connected enterprise services. Brazil and Mexico are important markets, supported by increasing cybersecurity awareness and demand for advanced threat detection and incident response capabilities. Organizations across the region are adopting managed security services and cloud-based SOC platforms to address ransomware, phishing, identity attacks, and data protection requirements. Limited cybersecurity talent availability also creates opportunities for AI-assisted monitoring, automated alert triage, and managed detection and response services.

Middle East & Africa

The Middle East & Africa region is expected to witness increasing adoption of AI Security Operations Center technologies, supported by digital transformation initiatives, government cybersecurity programs, and expanding investments in cloud and critical infrastructure security. Gulf Cooperation Council countries, including Saudi Arabia and the United Arab Emirates, are strengthening cybersecurity capabilities across financial services, energy, telecommunications, and public sector organizations. South Africa also represents an important regional market due to enterprise cloud adoption and cybersecurity modernization. Demand for AI-powered threat monitoring, automated incident response, and managed SOC services is expected to increase as organizations seek improved operational resilience and faster threat identification.

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Key Regions and Countries

North America

• US
• Canada

Europe

• Germany
• France
• The UK
• Spain
• Italy
• Rest of Europe

Asia Pacific

• China
• Japan
• South Korea
• India
• Australia
• Rest of APAC

Latin America

• Brazil
• Mexico
• Rest of Latin America

Middle East & Africa

• GCC
• South Africa
• Rest of MEA

Macroeconomic Impact

Cybersecurity spending has historically demonstrated resilience during economic contractions, and AI SOC investment reinforces that pattern. Enterprises facing headcount freezes view AI-augmented SOC tools as a cost-effective substitute for additional analyst hires. Rising interest rates between 2022 and 2024 compressed discretionary IT budgets but did not reverse security spending growth, as breach liability costs now outweigh the cost of prevention for most large organizations.

Currency volatility creates uneven pricing dynamics for multinational vendors selling AI SOC platforms in Asia-Pacific and Latin America. Dollar-denominated licensing costs rise in local terms when regional currencies weaken, slowing new contract formation in price-sensitive SME segments. Vendors offering local-currency pricing or consumption-based models absorb less churn in emerging markets during periods of dollar strength.

Market Dynamics

Driver: AI-Accelerated Attacks Outpace Human-Only SOC Response Capacity

The increasing sophistication of cyberattacks is encouraging enterprises to strengthen security monitoring through AI-assisted detection and response capabilities. Machine learning models can support event correlation, alert prioritization, and identification of suspicious activity across endpoints, networks, and cloud environments. AI-powered investigation tools can also help security teams process large volumes of alerts and reduce repetitive manual analysis. However, the effectiveness of these solutions depends on the quality of security telemetry, deployment configurations, threat complexity, and analyst oversight. Organizations are increasingly evaluating AI SOC technologies through measurable performance indicators, including detection accuracy, false-positive rates, investigation efficiency, and mean time to respond.

Restraint: Black-Box AI Logic Undermines Trust in Autonomous Containment

Security teams bear legal and regulatory accountability for containment actions taken in their environment. An AI agent that isolates a production server, blocks an identity, or severs a network segment without a verifiable evidence chain creates auditability risk that compliance officers cannot accept. Procurement stalls most often at the autonomous response stage, not the detection stage.

Prompt-injection attacks targeting AI agents embedded in SOC workflows represent an unresolved attack surface. Adversaries who can manipulate agent behavior through crafted inputs could subvert containment logic or suppress detections. Governance frameworks for agentic SOC deployment lag behind deployment timelines, leaving enterprises to self-define acceptable agent authority boundaries without industry consensus.

Opportunity: Mid-Market and OT-Specific AI SOC Packages Address Underserved Segments

Research published by IBM indicates that organizations with extensive use of AI and automation in security operations can experience lower breach-related costs and shorter breach lifecycles compared with organizations using limited or no security automation. These findings highlight the potential value of AI-assisted threat detection, investigation, and incident response. For mid-sized businesses and organizations with limited cybersecurity resources, cloud-based AI SOC platforms and managed detection services offer opportunities to strengthen security monitoring without proportionate increases in internal staffing. Nevertheless, financial and operational benefits vary depending on implementation maturity, security infrastructure, and organizational requirements.

Porter's Five Forces

Competitive intensity in the AI SOC market is high and rising. Supplier power is moderate but concentrated: foundation model providers, cloud hyperscalers, and specialized security data infrastructure vendors control inputs that no single AI SOC vendor replicates independently. Buyer power is increasing as enterprises move from pilot programs to multi-vendor evaluations, using head-to-head accuracy benchmarks to extract pricing concessions. The threat of substitutes is limited in the short term because no credible non-AI alternative exists for the alert volumes enterprise SOCs now face. New entrant barriers are meaningful but not prohibitive: proprietary threat intelligence datasets and existing customer telemetry relationships give incumbents a compounding data advantage, yet well-funded startups such as Exaforce secured $75 million in July 2026 specifically to challenge that advantage with purpose-built agentic architectures. Competitive rivalry is intense, with Microsoft, CrowdStrike, Palo Alto Networks, and IBM each pursuing platform consolidation strategies to capture the full SOC workflow rather than a single use case.

AI and Gen AI Impact

Generative AI and large language model agents are repositioning the SOC investigation workflow from human-executed to human-supervised. Microsoft's 2025 live-operations study found that Security Copilot adoption was associated with a 22.88% decrease in security alerts per incident three months after adoption, as reported by Microsoft across 378 adopter organizations versus matched controls. Analysts resolved incidents earlier in the attack chain, meaning AI did not simply speed up existing workflows. The tool changed where human judgment entered the process.

Microsoft's Phishing Triage Agent helped analysts detect malicious emails up to 550% faster in a randomized controlled trial involving 167 professional analysts, as confirmed by Microsoft. Triage and classification represent the highest-volume, lowest-complexity tasks in SOC workflows, making them the natural first target for Gen AI deployment. Vendors that automate triage at production accuracy free analyst capacity for threat hunting and investigation tasks that require contextual judgment that agents cannot yet reliably supply.

AI Adoption and Security Operations Efficiency

Artificial intelligence is increasingly influencing enterprise security operations by supporting automated alert classification, threat correlation, incident investigation, and response prioritization. AI-assisted security platforms can help reduce repetitive analyst workloads and improve the consistency of security monitoring activities. However, improvements in investigation time, alert accuracy, and operational efficiency depend on deployment conditions, data quality, threat complexity, and the level of human oversight maintained by security teams.

Research published by IBM indicates that organizations extensively using AI and automation in security operations can experience lower breach-related costs compared with organizations that do not use these capabilities. Independent cybersecurity threat assessments, including reports from ENISA and Verizon, also highlight the increasing complexity of attack techniques and the importance of effective detection, investigation, and incident response capabilities.

Vendor-reported productivity improvements should be presented as results from specific studies or customer deployments rather than as universal performance outcomes. Organizations should evaluate AI SOC solutions using measurable indicators such as mean time to detect, mean time to respond, false-positive rates, investigation accuracy, and operational cost efficiency.

Market Trends

Multi-Agent Orchestration Replacing Single-Copilot SOC Architectures

Multi-agent orchestration is the dominant near-term architectural shift. Buyers are moving from single-copilot deployments to coordinated networks of specialized agents handling triage, malware analysis, threat hunting, and response in sequence. Vendors that deliver agent orchestration layers with analyst-defined authority controls and natural-language detection engineering will capture disproportionate platform renewal and expansion revenue as enterprises deepen AI SOC commitments beyond initial use cases.

Market Competition Overview

The AI SOC market sits between consolidation and fragmentation. A small group of platform vendors, Microsoft, CrowdStrike, Palo Alto Networks, IBM, and Google, are extending existing security platform investments into AI SOC workflows, using customer data advantages and existing enterprise relationships to accelerate agent deployment. Platform incumbents win contracts by minimizing integration complexity and reducing the number of vendors a SOC team must manage.

Competition among AI SOC providers is increasingly focused on detection accuracy, investigation automation, platform integration, and the reliability of AI-assisted security decisions. Leading cybersecurity companies are expanding their capabilities through AI-powered alert triage, automated threat correlation, and incident response orchestration. Specialized vendors are differentiating their platforms through autonomous investigation agents, flexible deployment models, and integration with existing security infrastructure. However, vendor-reported performance results should be evaluated against independent testing, customer deployment conditions, and standardized security performance indicators rather than treated as universally achievable outcomes.

Pricing Analysis

AI SOC pricing structures vary significantly by deployment model and buyer size. Platform vendors price by endpoint count, data ingestion volume, or seat, with large enterprise contracts negotiated as multi-year commitments that include professional services, model tuning, and dedicated support. SOCaaS and MDR providers price on a consumption or monthly active endpoint basis, making costs variable and easier to justify in annual budget cycles without capital expenditure approval.

Downward price pressure is building as the vendor count increases and buyers run competitive evaluations using published accuracy benchmarks. Platform incumbents are bundling AI SOC agent capabilities into existing security product suites, effectively reducing the marginal cost of AI SOC for existing customers. Challengers must either match that bundled pricing or demonstrate outcome improvements significant enough to justify a separate line-item purchase.

Company Profiles

Microsoft Corporation

Microsoft is strengthening its position in AI-powered security operations through the integration of Microsoft Security Copilot with Microsoft Sentinel, Defender, and other enterprise security products. These solutions support automated incident investigation, threat intelligence analysis, security alert prioritization, and response coordination. Integration across Microsoft's security ecosystem allows organizations to correlate information from identity, endpoints, email, cloud environments, and enterprise applications. The operational effectiveness of these capabilities depends on deployment configurations, telemetry quality, analyst oversight, and the organization's existing cybersecurity infrastructure.

SentinelOne, Inc.

SentinelOne offers AI-powered cybersecurity capabilities through its Singularity platform and Purple AI security analyst technology. These solutions support natural-language security investigations, automated threat analysis, event correlation, and contextual interpretation of security alerts. Purple AI is designed to assist security analysts in identifying potential threats and reducing repetitive investigation activities across endpoint, cloud, and identity environments. Actual operational improvements vary according to implementation maturity, available security telemetry, the complexity of investigated incidents, and existing security workflows.

Key Players

• Microsoft Corporation
• Cisco Systems, Inc. (Splunk)
• CrowdStrike Holdings, Inc.
• Palo Alto Networks, Inc.
• Alphabet Inc. (Google Cloud / Google Security Operations)
• International Business Machines Corporation (IBM)
• Fortinet, Inc.
• Sophos Limited
• SentinelOne, Inc.
• Darktrace
• Vectra AI
• Exabeam
• Securonix
• Torq
• Dropzone AI
• Prophet Security
• Exaforce
• Arctic Wolf Networks
• Rapid7, Inc.
• Elastic N.V.
• Lumu Technologies
• Other Key Players

Supply Chain and Value Chain Analysis

The AI SOC value chain runs from foundation model providers and cloud compute infrastructure through security data platform vendors, AI SOC platform developers, channel partners and MSSPs, and ultimately to enterprise security operations teams. Maximum value creation concentrates at the AI platform layer, where proprietary threat intelligence, model training on customer telemetry, and agent orchestration logic determine detection accuracy and investigation quality.

The largest bottleneck sits at data ingestion and normalization. AI SOC agents require clean, correlated telemetry across endpoints, identities, cloud workloads, and network traffic to perform reliably. Enterprises running fragmented security stacks with inconsistent log formats face significant integration costs before agentic capabilities can operate at production accuracy. Vendors that offer pre-built connectors and automated normalization pipelines capture implementation budget that would otherwise stall deployment.

Regulatory Landscape

The EU AI Act classifies autonomous systems that affect critical infrastructure security decisions as high-risk, imposing transparency, auditability, and human oversight requirements on AI SOC vendors operating in European markets. SOC operators deploying agents with autonomous containment authority must maintain decision logs, provide model explainability documentation, and demonstrate conformance assessments before enterprise deployment. Vendors that build governance and audit infrastructure into their platforms before regulatory deadlines capture compliant-by-default positioning in EU procurement processes.

US federal agencies operating under CISA and NIST guidelines are moving toward AI security standards that mandate human approval for high-impact containment actions. Sector-specific regulators in financial services and healthcare impose additional incident reporting timelines that AI SOC platforms must support with automated evidence packaging. Regional data residency requirements in India, the UAE, and Southeast Asia add sovereign deployment complexity for vendors relying on centralized model infrastructure.

Investment and White Space Analysis

Investment is concentrating at the agentic orchestration layer, where the ability to coordinate multiple specialized agents across triage, investigation, and response workflows represents the next defensible moat. Mid-market SOCaaS packages combining managed expertise with consumption-based AI analyst capacity represent the clearest white space, as existing MDR providers lack the AI engineering depth and platform-native AI vendors lack the managed service delivery infrastructure to serve this segment credibly from either direction.

Purpose-built AI SOC agents for operational technology and industrial control environments remain underserved. Existing AI SOC platforms are trained predominantly on IT telemetry and lack the sector-specific investigation context needed for OT threat scenarios. Sovereign AI SOC deployments offering in-region models and data residency represent a high-value opportunity in Gulf Cooperation Council states, India, and Southeast Asian markets where cross-border data transfer restrictions limit cloud-hosted platform options.

Recent Developments

• July 2026 Exaforce raised $75 million in Series A funding led by Khosla Ventures, Mayfield, and Thomvest Ventures to develop its Agentic SOC Platform, which combines Exabots AI agents with advanced security-data exploration capabilities.
• June 2026 Cisco and Splunk launched purpose-built SOC agents for detection engineering, alert triage, malware analysis, and response automation, alongside Automated Threat Analysis for Splunk Enterprise Security Premier.
• March 2026 Cisco announced six specialized Splunk security agents, including the Detection Builder Agent, SOP Agent, Triage Agent, Malware Threat Reversing Agent, Guided Response Agent, and Automation Builder Agent, for phased release during 2026.
• February 2026 Splunk made Enterprise Security Premier generally available for cloud customers, combining Splunk Enterprise Security, SOAR, UEBA, and AI-assisted security operations in a unified SOC offering.
• November 2025 CrowdStrike launched Charlotte Agentic SOAR as the orchestration layer of the Falcon Agentic Security Platform, enabling native, custom, and third-party AI agents to coordinate security investigations and response under analyst-defined controls.

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