AI is absorbing the volume work that makes up the fundamental architecture of the Security Operations Center (SOC) tier system. While the tiers and the work aren’t going away, a junior and senior analyst’s day-to-day is changing fast.

At some point in the last week, every analyst on your team made the same call. Close an alert uninvestigated, because the queue was too long and triage ate the time real investigation and deep analysis was needed. Most of those calls were right, but odds are that at least one critical threat will eventually be overlooked.

The root cause here is the mathematical disparity. Nearly half of SOC teams lack the capacity to investigate more than fifty percent of the alerts they generate daily. Analyst capacity grows linearly while data volume compounds exponentially.

According to findings from SentinelOne®’s Annual Threat Report, what’s worse is that the math leans heavily towards the adversaries. Automated exploits have been recorded escalating privileges within a target environment in approximately 30 milliseconds. Similarly, malicious attack chains can progress from initial network access to establishing persistent footholds in under 50 seconds. No manual workflow currently matches these kinds of machine speed tempos.

As a result, the traditional tier structure of the SOC is transforming in real time. AI is already absorbing the triage and correlation work that that structure was originally built to manage. The critical call-out here is understanding that these tiers are not dissolving; rather, they are evolving. Junior and senior analysts still exist and hold their titles, and continue to have a clear career path ahead of them. What’s changing is the nature of the tasks that fills their day.

Analyst Tiers, Redefined by Depth

Historically, the distinctions between Level 1, Level 2, and Level 3 analysts were established primarily to manage high-volume workloads. Those distinctions were built to manage volume: Alerts routed to the right skill level, junior analysts escalating what they couldn’t resolve, senior time reserved for what actually needed it. AI now handles the triage and correlation volume those tiers existed to manage. Volume stops being the variable that defines the role. As a result, analyst tiers are being redefined by depth of expertise rather than the ability to process large queues.

That evolution isn’t limited to junior and senior analysts:

• Threat intel analysts move from manual feed correlation to directing AI-correlated intelligence
• Security engineers move from manual rule-writing to guiding AI-generated detection logic
• SOC managers move from tactical management to strategic leadership and AI governance

Depth of expertise replaces volume as the key differentiator: cloud architecture, identity, adversarial tradecraft. The kind of judgment that only comes from watching an environment long enough to know what normal looks like. AI can’t replicate nuanced, environment-specific skills that the legacy, volume-driven tiered system was never able to encourage or reward.

A Day in the Life, Before and After

In the legacy model, an analyst’s day typically begins by facing a queue containing hundreds of unvetted overnight alerts. Three or four hours go to manual triage, pivoting between tools to reconstruct what happened. The vast majority of the queue closes as false positives. The real threat, if there is one, surfaces hours later, pieced together across five or more disconnected consoles.

Adding in the administrative paperwork widens the gap even further. The legacy model requires analysts to spend upwards of an hour writing an incident report that adds nothing to the actual technical investigation. The new model allows the analyst to review and approve an AI-generated summary, adds environment-specific context, and closes the case in minutes.

In a modern model, the day starts with a prioritized queue instead of a noise wall: evidence-backed verdicts already assembled, ready for review. Thirty minutes go towards confirming the highest-priority case. That confirmation triggers a pre-approved response workflow within defined policy. Critical hours that were parcelled off to triage are used for proactive threat hunting instead.

Teams operating this way report the difference in hard numbers, according to two IDC research studies commissioned by SentinelOne.

• Teams using AI-powered investigation report 63% faster threat identification and 41% more efficient investigation. (IDC Business Value of Purple AI®, July 2025)
• AI SIEM customers on the Singularity Platform report 55% more efficient security operations and 4x more threats handled, at 55% lower solution costs. (IDC Business Value of Singularity AI SIEM, May 2026)

That time moves to where the judgment actually matters.

Governance is the New Core Skill

Recovered time only pays off if real judgment fills it. The most important judgment now is knowing when to distrust the AI.

The analyst who knows exactly where their AI is unreliable is more operationally effective than the one who trusts it uniformly. That skepticism is a skill and it has to be built on purpose, case by case.

Four capabilities define the analyst role going forward:

• Validating AI output instead of accepting it by default
• Designing the automation workflows that execute at machine speed
• Forming hypotheses worth hunting instead of only answering tickets
• Translating what the AI found into what it means for the business

Escalation frameworks must be designed to reflect that same judgment. Teams building trust in a new workflow route more cases to a human by default. Mature teams narrow that escalation path as their confidence in specific alert types grows. Either way, the analyst decides where the line sits, not the AI.

On top of this, analysts must govern the response earlier, setting the policy before events are triggered instead of reacting to it. Every automated action is scoped to a policy an analyst defined in advance. This keeps all of the details of the logged and fully auditable after the fact. The quality of what fires automatically traces back to the quality of that workflow.

The Career Path Forward

The evolution we are seeing in SecOps is directly addressing systemic issues of burnout and attrition, both significant risks to retaining talent within the cybersecurity industry. Under an AI-augmented model, every rung on the SOC career ladder gets more strategic:

• Junior analysts move from manual triage to verdict review
• Senior analysts move from reactive response to strategic hunting
• Managers move from daily firefighting to designing the system everyone else works inside

None of this happens in one leap. Adoption works crawl-walk-run, workflow by workflow. ‘Crawl’ starts with AI-assisted triage, validated against your own judgment, alert by alert. ‘Walk’ enables automated responses for well-understood, lower-risk cases, with human approval required for anything novel. ‘Run’ hands full workflows to AI for established threat patterns, with analyst time going to verdict review and hunting. Different parts of a SOC can sit at different stages of that maturity at the same time.

Start with the First 90 Days in the AI SOC checklist, a concrete plan for the next ninety days. For the full argument behind it, check out the Analyst’s Guide to the Autonomous SOC and see how SentinelOne is building toward this model.

Third-Party Trademark Disclaimer:

All third-party product names, logos, and brands mentioned in this publication are the property of their respective owners and are for identification purposes only. Use of these names, logos, and brands does not imply affiliation, endorsement, sponsorship, or association with the third-party.

Security Operations Centers (SOCs) are currently confronting scalability challenges on two fronts: structural and cognitive. The day-to-day reality of modern defensive operations is stark: an analyst frequently begins a shift facing a queue deeply saturated with unvetted alerts. To process a single event, the analyst must open the alert, pivot to a secondary console to complete an investigation, manually enrich an IP address, copy a file hash into a third interface, and cross-reference an asset inventory that may not have been updated in months. Following this, they must author and refine queries, waiting for overloaded databases to return historical context.

The actual work of assessing the investigation’s results and moving to decision-making and action has not even begun. This is the administrative burden of the modern SOC. The true threats are not just those that attempt to bypass defenses, but the critical operational hours lost before an active mitigation attempt is even initiated. While analysts are highly trained professionals, the relentless requirement to perform manual data aggregation inevitably leads to exhaustion.

Misdiagnosing the Bottleneck: The Upstream Data Problem

Threat actors operate at machine speed, utilizing automation to pivot laterally across networks in a matter of seconds, frequently disappearing before defensive teams can even log into their terminals. Expecting human defenders to counter automated threat vectors by manually aggregating bad data is an architectural failure.

Every SOC inherits a highly fragmented data ecosystem. Telemetry is continuously generated by diverse sources, including firewalls, cloud workloads, identity providers, endpoint sensors, and legacy systems. This telemetry arrives in disparate dialects, varying formats, and highly inconsistent levels of fidelity. Before AI tools can accurately reason about a potential threat, or an analyst can initiate a logical investigation and run a playbook response, this raw telemetry must be synthesized.

Historically, organizations analysts take on these complex synthesis processes, manually normalizing data points across different vendor schemas. This represents a key misallocation of human intelligence. The asymmetry in modern security operations is not merely a discrepancy in speed; it is an imbalance in how security teams are forced to allocate their finite time. When operators spend the majority of their shifts wrangling data instead of actively investigating threats, the foundation of the SOC itself is inadequate. To achieve defensive velocity, organizations must recognize that fixing the data foundation is the mandatory prerequisite for improving all downstream security functions.

Architecting the Data Foundation with Singularity AI Data Pipelines

Addressing the upstream data problem requires the implementation of advanced data pipelines capable of resolving enterprise data chaos before it impacts the detection engine. Frameworks such as SentinelOne’s® Singularity AI Data Pipelines serve as this foundational layer, engineered to ingest telemetry from every source and in every format without requiring months-long integration projects or heavy manual engineering.

Modern pipelines utilize AI to normalize raw telemetry into standardized formats, specifically aligning with the Open Cybersecurity Schema Framework (OCSF). This structural alignment transforms fragmented logs into structured data that is immediately actionable. It eliminates the need for analysts to construct complex regular expressions during critical incidents simply to reconcile how two different software vendors format data, such as usernames or a timestamp.

Efficient data ingestion also requires dynamic, in-flight optimization. Not all telemetry possesses the same analytical value, and storing all generated logs in highly indexed, expensive storage tiers is financially and operationally untenable. Data pipelines optimize data streams by filtering out extraneous noise, trimming excess volume, and routing specific logs based on dynamic criteria. High-value security events are routed and indexed for rapid search retrieval, while lower-priority compliance or operational logs are routed to more cost-effective tiered storage. The result is a substantial reduction in infrastructure costs, a higher signal-to-noise ratio, and a structured data foundation that is completely prepared the moment an investigation is required.

When underlying data pipelines automatically enrich that log with identity and asset information, revealing (for example) that a specific financial director’s laptop in a remote office is communicating with a known botnet, the output transitions from a raw data point into a definitive starting point. Crucially, this enrichment occurs systematically before the human operator ever interacts with the alert. Solving this data problem end-to-end is a primary reason SentinelOne was recognized in the IDC MarketScape for AI SIEM.

Accelerating Detection via Singularity AI SIEM

When a clean, structured data foundation is properly established, the performance of downstream security tools accelerates. Modern detection engines, such as the Singularity AI SIEM, leverage indexless architectures to manage enterprise-scale telemetry. Because the data is normalized and optimized prior to ingestion, these platforms can execute petabyte-scale queries with minimal latency, ensuring investigative results are delivered before the analyst’s attention wanes.

Within this architecture, detection logic is executed continuously against a stream of clean, correlated telemetry. This transforms an ocean of disparate event logs into readable, centralized dashboards that provide immediate situational awareness. The quantitative benefits of this approach are substantial. With AI SIEM, organizations are already executing their queries 70% faster. Adding AI Data Pipelines further augments this workstream, providing cleaner data for AI to run at optimal efficiency. These improvements represent the direct result of ensuring that the data arriving at the SIEM is inherently fit for purpose.

AI SIEM remains a single, comprehensive SKU with customers automatically receiving integrated pipeline functionality for everyday data optimization rather than treating it as a premium add-on. For every unit of paid Data Ingest capacity, customers can process twice that volume through Data Pipelines. A customer with 500 GB/day SIEM entitlement can push 1 TB/day through the pipeline at no additional cost.

Transitioning to Agentic Reasoning Layers with Purple AI

The establishment of a structured data pipeline unlocks the capability for true agentic reasoning within the SOC. Unlike traditional rule-based automation, which executes static responses to predefined triggers, technologies like SentinelOne’s Purple AI operate as a dynamic investigative layer.

When an initial alert is generated, an agentic reasoning system does not simply pause and wait for human triage. It autonomously launches an investigation, comprehensively maps the potential blast radius of the incident, and synthesizes a clear, logical recommendation for containment. Then, the analyst logs into the console and is presented with a fully formed situational briefing rather than a blank investigation screen.

More importantly, an agentic AI layer possesses the capacity to evaluate broader adversarial campaigns rather than isolated security events. In isolation, a minor registry key modification, a singular file write, or a brief outbound network connection may not meet the threshold for a critical alert. Legacy security tools often fail to connect these disparate, low-signal events. However, Purple AI can assemble these seemingly unrelated activities into a cohesive narrative, exposing the overarching strategy of the attacker before a major breach occurs.

This level of autonomous intelligence is strictly dependent on the underlying architecture. Advanced AI algorithms cannot derive accurate conclusions from unparsed, low-quality telemetry. The analytical integrity of the agentic layer is entirely contingent on the principle of data quality; systems like Purple AI require clean, structured data to function effectively, avoiding the fundamental issue of “garbage in, garbage out”.

Governed Hyperautomation and the Human-in-the-Loop

The final component of a modernized, agentic SOC is the deployment of Hyperautomation to execute defensive responses. To counter threats effectively, organizations must deploy automated workflows capable of executing decisions at machine speed. These no-code workflows can be configured to trigger autonomously based on AI triage verdicts, the disclosure of new high-severity vulnerabilities, or specific incoming alerts. By automating the mitigation phase, the SOC evolves from an environment strictly dedicated to passive observation into a dynamic system that actively neutralizes threats.

However, the implementation of automated response mechanisms must be rigorously governed. Executing changes to enterprise infrastructure carries inherent risk. To mitigate this, automated workflows must integrate critical approval steps, ensuring that highly consequential actions are paused until human authorization is provided. The analyst retains the ultimate authority, defining the precise parameters of what processes may run automatically and what workflows require manual judgment.

Redefining the Analyst Mandate via Autonomous Security Intelligence

The strategic objective of integrating data pipelines, agentic reasoning, and Hyperautomation is not the removal of the human operator. Instead, the overarching goal is the restoration of the analyst’s primary function: exercising expert judgment.

By offloading repetitive tasks to technological systems, organizations systematically remove operational friction. The data layer filters out irrelevant noise, allowing the analyst to clearly see the threat. The AI investigation layer removes the administrative grind of data collection, allowing the analyst to focus purely on analytical thinking. Finally, the automated response layer eliminates procedural delays, ensuring the analyst’s decisions are executed rapidly enough to matter. This creates an intelligence fabric, known as Autonomous Security Intelligence (ASI), where data, investigation, and response function concurrently as a single, unified system.

Under this model, the operational output of a single analyst is exponentially multiplied, allowing one unburdened professional to accomplish the work of ten while still owning every critical decision. While the alert queue will perpetually require attention, the fundamental nature of the work fundamentally changes. The timeline of a manual initial triage to active investigation compresses from a multi-hour ordeal into a matter of minutes. The data arrives clean, the investigation runs automatically, and the response mechanisms are prepared. The hours previously consumed by administrative waiting are directly reallocated to strategic decision-making.

Conclusion

When defensive systems are finally architected to operate at the speed of the modern threat landscape, the role of the human operator transforms. Analysts are no longer forced to act as passive passengers, grateful to be carried by fragmented tools. They are elevated to the role of pilots, operating with full situational awareness, retaining their judgment, and actively directing the defensive posture of the organization. This is the paradigm of the agentic SOC, and it is entirely predicated on the foundation of clean, structured data.

Contact us today to learn more about how SentinelOne is leading the way forward with Agentic SOC.

The Good | Authorities Sanction Cybercriminals & Dismantle Russian Bulletproof Hosting Infrastructure

The EU and the United Kingdom have jointly sanctioned multiple Russian individuals and entities for targeting government networks and critical infrastructure across Europe. The sanctions specifically target senior Russia military intelligence (GRU) officers and operators, as well as four entities linked to the Federal Security Service (FSB).

Officials say that the Russian government actively utilizes these state-sponsored units alongside recruited cybercriminals and private companies to systematically destabilize international partners and compromise key infrastructure across the continent.

From the U.S. Treasury Department, two individuals and a virtual private network (VPN) provider face sanctions for actively enabling ransomware attacks against American organizations.

OFAC designated First VPN Service (1VPNS) and its administrator, Dmytro Rashevskyi, for supplying infrastructure that helped cybercriminals obscure their identities and manage stolen data. The service, which law enforcement dismantled last May, notoriously ignored abuse complaints and maintained zero user logs.

Yegeniy Silayev was also sanctioned for developing cryptors designed to conceal malware. Investigators estimate these specific tools and services directly facilitated billions of dollars in financial losses across critical sectors.

U.S. Federal prosecutors also unsealed indictments this week against three Russian nationals for operating bulletproof hosting services that facilitated over $62 million in global ransomware damages.

Defendants Aleksandr Volosovik, Yulia Pankova, and Kirill Zatolokin allegedly managed “Media Land” and “ML Cloud”, providing essential infrastructure to syndicates like Lockbit, Play, and Blacksuit. These hosting platforms actively shielded cybercriminals by disregarding victim complaints and ignoring law enforcement takedown requests.

To disrupt this supply chain, the State Department is offering a $10 million reward for actionable information regarding foreign government links to these hosting providers.

The Bad | Attackers Trojanize Popular Remote User Platforms to Deploy Starland Malware

Cybersecurity researchers identified a financially-motivated Russian threat actor tracked as UAT-11795. Active since June 2025, the actor has utilized trojanized applications to harvest user credentials and cryptocurrency while primarily targeting users across the United States, Germany, Romania, and Venezuela.

To distribute their payloads, UAT-11795 operators disguise malicious installers as legitimate software, including WebEx, Zoom, MobaXterm, DBeaver, and FaceIT. Researchers suspect the attackers likely deploy these files via ClickFix social engineering.

The infection chain typically starts when a victim executes a malicious HTA file. This file retrieves an altered NSIS installer harboring a hidden Python loader disguised as a standard text document. The loader then modifies the Windows Registry to ensure persistent access before decrypting and deploying the Starland remote access trojan (RAT).

Upon execution, Starland verifies whether it is operating within a sandbox before creating scheduled tasks and attempting to escalate its system privileges. The malware scans compromised systems for browser data, cryptocurrency wallet assets, detailed system configurations, any antivirus products, and Active Directory infrastructure such as domain structure and controllers.

Beyond data theft, Starland possesses extensive capabilities to capture desktop screenshots, execute arbitrary shell commands, and fetch secondary payloads. Depending on system architecture, the malware can inject a 64-bit shellcode chain to deliver the CastleStealer information stealer or a 32-bit chain to deploy the Remcos remote access trojan.

To maintain resilient command and control (C2) communications, the operators integrate a redundancy mechanism that queries a Polygon smart contract for a fallback domain, and control two Telegram bots to receive notification beacons, including messages with the victim’s machine fingerprints and cryptowallet inventories.

Users are reminded to avoid executing unidentified commands online and should only download confirmed software from official vendor sources.

The Ugly | Nearly 300 Imposter GitHub Repositories Distribute Infostealing Malware to Collect Sensitive Data

Threat actors have published almost 300 fabricated GitHub repositories to distribute an information stealer from the BoryptGrab malware family. The actors systematically impersonated premium security products, cryptocurrency tools, and developer utilities to deceive victims searching for free software downloads.

As part of the lure, the malicious landing pages employ highly sophisticated client-side scripts that parse referral URLs to render customized branding and spoofed trust badges, significantly increasing the likelihood of successful social engineering.

Once a targeted victim clicks the download link, the infrastructure delivers a constantly rotating ZIP archive containing a legitimate, signed WinGUP updater paired with a trojanized dynamic link library file. When the user executes the updater, the program side-loads the malicious file, which then decodes and reflectively executes the BoryptGrab-variant payload directly into system memory.

Operating without establishing long-term persistence, the malware is designed to exfiltrate maximum data in a single execution cycle. The stealer targets passwords, payment details, and session cookies across 19 different web browsers and 32 cryptocurrency wallet brands, alongside messaging tokens from Discord, Steam, and Telegram.

To maximize collection, operators utilize direct code injection to bypass Chrome’s native App-Bound Encryption. All newly harvested data is compressed and routed to a Russian-based C2 server. Although the malware leaves behind forensic evidence by failing to wipe temporary staging directories, the scale of the impersonation campaign poses significant risks to unsuspecting developers.

GitHub has already removed a large portion of the false repositories, though several of the malicious redirector pages remain actively online. Researchers advise users to independently verify software authenticity and exercise extreme caution when navigating unofficial portals, sharing this YARA rule to help detect BoryptGrab activity and IoCs.

The Good | Authorities Apprehend Pro-Russian Hacktivist & Dismantle Global Fraud Networks

Spanish authorities, acting on intelligence provided by the FBI, have apprehended a suspected core member of the pro-Russian hacktivist syndicates CyberArmy of Russia Reborn (CARR) and Z-Pentest.

While masquerading as ideologically motivated collectives, these groups have actively executed disruptive cyberattacks against critical infrastructure, including food processing and water facilities across the United States and Europe. Investigators allege the arrested individual, residing in Palencia, provided extensive operational and logistical support to a Ukrainian hacker working for CARR, even attempting to facilitate their escape to Russia.

The individual is also suspected of coordinating cyber operations for the NoName057(16) group using encrypted messaging platforms. In a March 2026 raid, law enforcement officers seized multiple computers and successfully froze cryptocurrency wallets utilized to launder illicit proceeds generated from stolen data sales. The suspect currently faces ongoing criminal investigations for alleged collaboration with a recognized terrorist organization and severe computer damage.

In a massive global crackdown on social engineering and financial fraud, international law enforcement agencies have arrested 5,811 suspects and seized approximately $293 million in illicit assets. Codenamed “Operation First Light 2026”, the coordinated initiative spanned 97 countries and specifically targeted business email compromise (BEC), investment scams, and money laundering syndicates operating between January and April. Interpol actively coordinated the extensive joint action, collaborating directly with regional policing bodies like ASEANAPOL, GCCPOL, and Europol to swiftly block over 31,000 fraudulent bank accounts and virtual wallets.

Investigators identified more than 142,000 victims worldwide and pinpointed an additional 15,600 suspects for future prosecution. This success builds upon recent international efforts, including Operation Synergia II, to dismantle the sprawling infrastructure supporting transnational cybercrime. Officials emphasize that robust, cross-border law enforcement cooperation remains essential to combat the escalating threat of organized cyber-enabled financial crimes globally.

The Bad | Threat Actors Deploy Forg365 PhaaS to Hijack Microsoft Accounts

Cyber researchers have identified a new phishing-as-a-service (PhaaS) operation dubbed Forg365, which targets Microsoft 365 enterprise accounts. Blending adversary-in-the-middle (AiTM) techniques with device-code phishing, the platform provides an integrated dashboard to manage post-compromise activities.

Forg365 works by incorporating AI to assist in generating customized phishing lures. By integrating AI directly into the control panel, Forg365 developers significantly lowered the financial cost needed to launch targeted campaigns. To remain undetected, its operators route messages through legitimate Amazon SES infrastructure while hosting their landing pages on Cloudflare.

The operation leverages the OAuth 2.0 device code authentication flow, originally designed for input-constrained devices. Attackers present victims with a deceptive verification page, tricking them into authorizing an attacker-controlled gadget rather than stealing their password directly.

Once initial access is achieved, the platform ensures persistence through a specialized browser extension called ForgCookie. Compatible with Microsoft Edge, Google Chrome, and Brave, this extension operates silently to request account data, clear session cookies, and trigger a hidden OAuth flow to capture fresh tokens. Doing so grants attackers continuous access to the victim’s Microsoft services without requiring them to ever re-authenticate.

To protect its administration panel from being accessed by security defenders, Forg365 integrates robust anti-analysis features. The platform utilizes debugger traps, polymorphic code, and dynamic sandbox checks to evade detection, redirecting connections to innocuous websites whenever a VPN is detected.

Administrators can defend against these hijacking techniques by monitoring Microsoft Entra logs for unexpected device-code authentication events. Organizations can also restrict or entirely disable device-code flows unless absolutely necessary. In the event of a suspected compromise, security teams should revoke all OAuth grants and refresh session tokens to sever access.

The Ugly | Rival Espionage Actors Breach & Spy On Pakistani Law Enforcement Networks

Between February 2024 and April 2026, suspected state-sponsored threat actors based in China and India separately converged on several Pakistani law enforcement organizations in unrelated cyberespionage campaigns.

According to SentinelLABS, operators heavily targeted the Balochistan Police, compromising critical network appliances and web servers. By infiltrating these critical systems, both nations actively sought independent visibility into Pakistan’s internal security posture and ongoing counter-militancy operations.

The China-nexus intrusions, leveraging PlugX, ShadowPad, and Cobalt Strike malware, were likely driven by Beijing’s concerns over the safety of Chinese nationals working on regional infrastructure projects within the China-Pakistan Economic Corridor (CPEC). Ongoing terrorist attacks have left the Chinese government dissatisfied with Pakistani protection and data from Balochistan Police would give the PRC direct insights.

Conversely, the India-nexus activity utilized Remcos backdoors to gather intelligence on the restive Balochistan province, a recurring flashpoint in the adversarial relationship between the two countries. Control over Balochistan Police networks means having invaluable visibility on how Pakistan manages their security posture as well as persistent access to civilian data.

One China-aligned threat actor specifically compromised the Balochistan Police Force’s Complaint Management System (CMS), a web application that actively serves both law enforcement personnel and Pakistani civilian users. The attackers uploaded custom malware implants disguised as routine portal updates, effectively weaponizing the digitalization of public policing services.

One payload masqueraded as a legitimate component of endpoint security software to evade initial detection and deploy an AsyncRAT client in order to establish persistent footholds into internal police networks while simultaneously surveilling citizens utilizing the platform.

This multi-actor convergence highlights how modernized policing infrastructure can serve as a high-value intelligence target for rival nations seeking comprehensive regional data.

The US Federal Government is committing $600 million to build one of the world’s most advanced AI infrastructure systems. Executive Order 14363, the Genesis Mission, connects national laboratory supercomputers across nuclear simulation, biodefense, energy grid modeling, and every major scientific domain. Fifty-one organizations signed on, including NVIDIA, OpenAI, IBM, Microsoft, AWS, Google, and Oracle.

The security framework governing these workloads was not written for this scale of use.

NIST SP 800-234, the High-Performance Computing Security Overlay, is well-constructed, tailoring 60 controls across four security zones, building on the SP 800-53B moderate baseline. It was designed for deterministic HPC workloads such as climate simulations, finite element analysis, and computational fluid dynamics. These workloads share a common attribute: code that runs the same way, every time, and behaves predictably under well-understood inputs. The security controls governing those workloads assume you can scan at the perimeter, clear memory between jobs, and attest to integrity at load time.

AI workloads break every one of those assumptions.

SentinelOne has submitted a formal proposal to the NIST HPC Security Working Group regarding this gap, and NIST has acknowledged it. We have a post on LinkedIn to share our proposal, and welcome commentary from across the industry.

The supply chain problem just got a lot more dangerous

This spring, in just three weeks, three AI-driven supply chain attacks targeted widely deployed software: LiteLLM, the most-used AI infrastructure package in Python development environments, Axios, the most-downloaded HTTP client in the JavaScript ecosystem, and CPU-Z, a trusted system diagnostic tool with a legitimate signed binary from the official vendor domain.

SentinelOne stopped all three on the same day each attack launched, with no prior knowledge of any payload.

The most important aspect of this outcome is how these attacks were stopped, and why signature-based detection couldn’t work. Each attack arrived through a trusted delivery channel. LiteLLM was compromised after credentials were stolen via Trivy, a security scanner. The attacker published two malicious versions to the PyPI repository. In at least one confirmed case, an AI coding agent with unrestricted permissions auto-updated to the infected version, meaning there was no human review or approval step before the payload ran. The Axios attacker exploited a legacy access token that the project maintainers had forgotten to revoke, bypassing every npm security control. CPU-Z attackers targeted the vendor’s distribution infrastructure directly; anyone who downloaded from the official website received a properly signed binary containing a payload. In all three cases, while the authorization chain was legitimate, the intent was not.

This is the defining characteristic of modern supply chain attacks: the workflow is verified, but the intent has been subverted. Every perimeter control, signature library, and reputation lookup checks authorization and passes. These attacks were designed to exploit that gap, and they ran at machine speed through automated pipelines with no human checkpoint.

To put this into the context of HPC and AI workloads running at scale, a compromised Python package in a developer’s environment is a serious incident; a poisoned training pipeline on classified biodefense data on a national laboratory supercomputer is on a different order of magnitude. The model it produces may be correct 99.9 percent of the time and adversarially wrong under precisely targeted conditions. No perimeter scan, signature check, or load-time integrity verification will catch it after training completes.

Where the current framework falls short

Of the 60 controls SP 800-234 tailors, three bear directly on AI workload protection, and each carries a documented gap. In a fourth area, supply chain, the overlay does not tailor at all.

• SI-3 (Malware scanning): The control acknowledges that real-time scanning is most effective but explicitly permits tailoring for performance on HPC systems, deferring to perimeter scanning before data reaches the compute zone. For traditional HPC workloads, that tradeoff may be defensible, but for AI workloads, it leaves behavioral analysis of the execution process completely unaddressed. A poisoned training run that executes within the expected statistical range of a training job looks like legitimate compute to a perimeter scanner.
• SI-4 (System monitoring): The control notes that high-speed data flows in HPC environments can overwhelm standard monitoring tools, and lacks AI-specific monitoring requirements or telemetry collection requirements from execution pipelines. The practical interpretation of this is: monitor what you can, accept the gap for what you can’t. On infrastructure running AI at scale, that gap creates a primary attack surface.
• SC-4 (Information in shared resources): Requires GPU memory clearing between user reassignments. It addresses data residency at the transition but does not address runtime behavioral monitoring of workloads during execution, side-channel attack detection, or anomalous compute-pattern identification while training is active.
• SR family (Supply chain risk management): The overlay carries all 12 moderate-baseline SR controls forward from SP 800-53B, with no HPC or AI-specific guidance, and supply chain is not among the 14 categories it tailors to. The SR controls still address only the conventional software and hardware supply chain; they say nothing about training-data provenance, model-weight integrity, or pre-trained-model validation, and the framework defines no AI equivalent of a software bill of materials. LiteLLM, Axios, and CPU-Z all arrived through legitimate software supply chain channels. AI workloads carry that same exposure one layer deeper, in the data and model artifacts that software trains on, which is exactly where the overlay is silent

AI Runtime Threats

The attacks against AI workloads on HPC are not theoretical, and they are not detectable at the perimeter.

• Training data poisoning scales at rates most security teams are not equipped to respond to. Research¹ across 41 studies documents attack success rates exceeding 60 percent from manipulation of 100 to 500 training samples, a fraction of a percent of a typical dataset. Poisoning as little as 3 percent² of training data achieved 41 percent attack success rates in code-generating models. OWASP’s LLM Top 10³ documents the consequence. Backdoors leave model behavior intact until a specific trigger activates adversarial outputs. The model ships, it gets deployed, and operates correctly, until it doesn’t. No post-training audit reliably catches a well-designed poisoning attack.
• GPU side-channel attacks are executed remotely by a co-tenant workload on shared GPU infrastructure; no physical access is required. The NVBleed research demonstrated covert channel attacks on NVIDIA NVLink, achieving over 91 percent accuracy in recovering data-dependent information from co-tenant GPU workloads on a shared fabric. The BarraCUDA research demonstrated the extraction of neural network weights via electromagnetic side channels from NVIDIA hardware. Both attack classes execute during active training, not at job transition. If your HPC environment runs multiple projects or security classifications on shared accelerators, the co-tenancy model is an active attack surface today.
• Inference pipeline compromise survives load-time integrity checks. A model with clean weights at deployment faces attacks through three vectors: hot-swap modification of serving configurations while inference runs; preprocessing and postprocessing layer injection that alters inputs before they reach the model or modifies outputs before delivery; and adversarial input manipulation that triggers targeted misbehavior in a model that appears fully operational. For AI serving safety-critical inference, each is a security risk, not just a research concern.

The characteristic that makes AI workloads uniquely difficult is persistence. A compromised simulation may produce visibly wrong results, but a compromised model can produce correct results the overwhelming majority of the time and adversarially wrong results under precisely targeted conditions. By the time anyone has reason to investigate, the window for recovery has often closed.

Securing HPC AI Workloads

We know the technology required to address these gaps exists and has been proven at scale in environments with performance constraints far tighter than those in HPC. What is needed is a well-defined architecture that enables the secure execution of large-scale AI workloads.

Dedicated security compute. Runtime security that shares CPU resources with the workload it monitors can be starved of CPU time under heavy load and interfered with by a workload that achieves kernel-level access. The SPiCa research demonstrated that eBPF monitoring pipelines can be manipulated from within the kernel by rootkits filtering events before they reach the analysis engine, meaning that a co-scheduled monitor is not a reliable monitor.

Every other infrastructure function on an HPC node has dedicated resources. The job scheduler, the filesystem client, and the out-of-band management plane. Security monitoring is infrastructure and should be afforded the same dedicated resources.

Modern HPC nodes have 128 to 256 CPU cores. One reserved for security monitoring is less than one percent of the available compute. Linux kernel CPU isolation via isolcpus, nohz_full, and rcu_nocbs is production-proven in high-frequency trading and real-time systems, with bounded, predictable overhead.

eBPF-based behavioral telemetry at the training layer. Effective monitoring of an AI training pipeline means continuous observation of compute behavior profiles, memory access patterns, GPU utilization, and inter-node communication, with behavioral baselines established for approved training configurations. A poisoning attack that executes within expected statistical ranges is not visible to a perimeter scanner, but it is visible to a behavioral baseline that knows what the training job should look like.

This is the same principle that SentinelOne’s on-device Behavioral AI detected for LiteLLM, Axios, and CPU-Z. The LiteLLM detection flagged a Python interpreter executing Base64-decoded code in a spawned subprocess. The CPU-Z detection flagged an anomalous process chain: cpuz_x64.exe spawning PowerShell, which spawned csc.exe, which spawned cvtres.exe. CPU-Z doesn’t do that. The behavioral baseline knew what legitimate execution looked like, and in these cases, that behavior was the decisive signal.

Cloudflare uses an eBPF-based architecture to mitigate DDoS attacks exceeding 7 Tbps. SentinelOne uses it to detect and stop threats in under one second across enterprise fleets. A training job that begins writing to unexpected locations, establishing anomalous inter-node communication, or deviating from its expected compute profile is detectable at runtime, before the model completes training. The performance argument against runtime monitoring on HPC was never about the technology; it requires a shift in architecture.

Inference-time output monitoring. Deployed models require continuous observation of output distributions, latency patterns, confidence score distributions, and input-output statistical properties. A model under adversarial input attack, or serving modified weights, exhibits detectable output patterns before any human analyst notices the outputs are wrong. Circuit-breaker logic needs to be designed into the serving architecture, not added after the first incident.

Model integrity verification that runs during inference. Load-time attestation is a necessary and important requirement; it is not sufficient. Long-running inference deployments are vulnerable to hot-swap attacks that replace weights after the initial integrity check passes. Continuous cryptographic hash verification of loaded model weights, running on the dedicated security core with automated circuit-breaker logic on failure, closes that vector. For a model serving safety-critical calculations, the re-verification frequency should match the workload’s risk profile with predictable overhead.

An SR-family extension for the AI supply chain. The existing SR controls address software supply chain risk. They do not address training data provenance, model weight integrity at ingestion, or pre-trained model validation. An AI bill of materials, including cryptographic documentation from the training data source through intermediate checkpoints to the deployed model, is the model-layer equivalent of software supply chain controls. Without it, every pre-trained model loaded into an HPC environment is an unverified artifact from an unverified chain.

Defending AI at Every Layer

The supply chain attacks this spring demonstrated what happens when defense architecture falls behind the delivery mechanisms attackers use. LiteLLM, Axios, and CPU-Z all arrived through trusted channels, carrying payloads no signature database contained. They were stopped because behavioral detection does not require prior knowledge of the payload. It requires knowing what legitimate execution looks like and acting when execution deviates.

Defenders protecting AI workloads face that same problem across every layer they own. HPC is the hardest version of it. But identities, endpoints, applications, and infrastructure all carry the same exposure at different scales. SentinelOne gives defenders coverage across all four, with behavioral AI running at each layer to catch what signatures miss. The specifics of how that works across your AI environment are in our AI security overview.

Citations

¹ “Data Poisoning 2018–2025: A Systematic Review. IACIS (2025)”, and “Data Poisoning Vulnerabilities Across Health Care AI Architectures. JMIR (2026)

² “Poisoning Attacks on LLMs Require a Near-Constant Number of Poison Samples” (2025). arXiv:2510.07192 and Huang et al., 2020.

³ OWASP (2025) LLM04:2025 Data and Model Poisoning. OWASP Gen AI Security Project.

The Good | Authorities Apprehend Iranian Cybercriminal & Extradite UNC3944 Hacker

Montenegrin law enforcement, alongside the FBI, have apprehended a 39-year-old dual Iranian and Turkish citizen wanted by the U.S. government for several cybercrime offenses. Arrested in Kotor, the suspect faces charges in the Southern District Court of New York for conspiracy to commit computer fraud, hacking, and identity theft.

Since 2013, this individual allegedly orchestrated mass cyberattacks against more than 150 American universities and inflicted damages estimated at over $3.4 billion. Investigators say the stolen data and compromised academic credentials directly benefited the Islamic Revolutionary Guard Corps and various Iranian state entities. The case now proceeds to a High Court judge for formal extradition hearings. The arrest follows recent warnings from U.S. cybersecurity agencies regarding escalating Iranian state-sponsored operations targeting critical domestic infrastructure.

A 19-year-old dual United States and Estonian citizen, Peter Stokes, has been extradited to face federal charges for his role as a core member of the UNC3944 (aka Scattered Spider, oktapus) cybercrime syndicate. Finnish authorities initially apprehended Stokes at the Helsinki airport as he attempted to board a flight to Japan. Prosecutors are accusing him of orchestrating multiple high-profile corporate breaches, using intense social engineering tactics against IT helpdesks to bypass multi-factor authentication controls.

In one notable May 2025 incident, UNC3944 compromised a multibillion-dollar retailer, demanding an $8 million ransom while inflicting over $2 million in operational disruption and remediation costs. UNC3944 operators are also responsible for more than 100 network intrusions globally, all of which yield upwards of $100 million in illicit extortion payments. Stokes remains in federal custody in Chicago, facing charges of fraud, conspiracy, and computer intrusion.

The Bad | Russian Intelligence Exploit Phishing Campaigns To Steal Signal Backup Keys

CISA and the FBI are warning that Russian state-sponsored threat actors have made large strides in evolving their phishing operations to target the backup recovery keys of Signal users. In an update to their March 2026 advisory, the two agencies attribute this ongoing activity to Russian Intelligence Services (RIS), including Russia’s Federal Security Service (FSB) Border Guards and the country’s military.

Tracked as UNC5792 and UNC4221, these campaigns specifically target high-value individuals, including government officials, military personnel, journalists, and policy analysts. Previously, operators focused on harvesting standard verification codes or tricking users into silently linking unauthorized devices. Now, they employ social engineering to access private communications without compromising the application’s underlying end-to-end encryption.

During targeted intrusions, attackers masquerade as official Signal support personnel and send direct messages falsely claiming the platform requires mandatory two-factor verification following alleged international cyberattacks. The operators systematically guide victims through the specific process of enabling the Secure Backups feature, instructing them to paste their newly generated recovery key directly into the chat interface.

Once adversaries obtain this critical key, they seamlessly download and decrypt the victim’s entire historical message archive onto their own controlled devices. Simply registering a new account under the same phone number does not natively invalidate a compromised key – users must actively generate a new backup key within their application settings to effectively secure future communications.

The U.S. Department of State recently announced a substantial reward of up to $10 million for information leading to the identification or location of these operatives. Through the Rewards for Justice program, federal authorities actively seek actionable intelligence regarding the syndicates’ operational infrastructure, illicit funding mechanisms, and direct affiliations with Russian intelligence services.

The Ugly | Unknown Hackers Breach Department of Homeland Security Information Network

The Department of Homeland Security is actively investigating a cyberattack that recently compromised its Homeland Security Information Network (HSIN). The network is an information sharing platform used by federal, state, local, and private-sector partners, specifically to share sensitive but unclassified data amongst the government and internationally.

According to an initial report, an unidentified threat actor orchestrated the intrusion between late May and early June. Investigators indicate that the attackers targeted the HSIN’s core servers alongside a SharePoint environment designed for extensive interagency collaboration.

While officials have not yet attributed the breach to a specific foreign government or syndicate, the full extent of data exposure remains unclear. The compromised platform routinely supports real-time incident management, intelligence exchange regarding persons of interest, and operational coordination. Because the United States is overseeing security for World Cup matches across the country, experts raise concerns that the intrusion could have exposed critical security planning, response procedures, and communication protocols.

In a public statement to the press, a departmental spokesperson confirmed the incident, clarifying that the breach strictly involved an unclassified legacy information-sharing environment. Security personnel promptly isolated the affected systems and mitigated the underlying vulnerability before starting forensics.

Officials emphasized that the attack did not impact classified networks, and the primary system remains operational for authorized partners. As the investigation continues, authorities strongly urge U.S. government staff, contractors, and associated partners to remain vigilant while defense teams harden the underlying infrastructure against further unauthorized network access attempts.

When SentinelOne® introduced the Autonomous SOC maturity model, we made a deliberate choice: describe a journey, not promise a destination.

The industry had no shortage of vendors declaring that AI would transform security operations. We thought the more useful contribution was a framework for understanding what that transformation looked like, at what pace it was realistic, and what conditions each stage of progress required.

Security teams found the model useful. Not as a marketing claim, but as a map. CISOs and SOC leaders started placing their organizations on it, asking what it would take to move forward.

What happened next was telling. By RSAC 2026, ‘autonomous SOC’ appeared in vendor keynotes and product launches from companies that hadn’t used the term twelve months earlier. Add in pseudonyms like Agentic SOC and AI SOC, and the list explodes. Fast adoption brings loose definitions. For us, it’s worth being precise about what the concept means and what it doesn’t.

Here is what SentinelOne has learned from 18 months of real-world Autonomous SOC deployments.

What Held Up

Today, the progression still maps accurately to where organizations are and what separates each stage from the next. That accuracy holds even for a framework built before most organizations had meaningful AI deployment experience. The inflection points reflect real operational transitions at each maturity step.

The “journey not destination” framing has proven more important than we anticipated when we wrote it. In early 2026, Gartner published guidance to help buyers evaluate AI SOC claims more critically, noting that vendor credibility in this space depends on honest representation of where the technology is:

A maturity model is structurally honest. It reflects where you are, not where a vendor wishes you were. Gartner’s research found that while 40% of organizations are actively evaluating AI SOC capabilities, only 18% have actually deployed₂. The gap between evaluating and deploying is rarely about technology. Most organizations cannot advance because they lack a clear view of where they stand or what the next stage requires.

When security leaders use the model as a reference point, the evaluation conversation changes. The question shifts from “does your product make my SOC autonomous?” to “what would it realistically take to advance, given where we are today?” A feature list cannot answer that question. An honest vendor can.

Watch our webinar on why most AI SOC deployments stall here.

What We Underestimated

The levels were always sound. What we underestimated was how much organizations needed to build before they could operationalize them. Customers understood where they wanted to go. But achieving Partial Autonomy (Level 3) requires a data foundation, a workflow architecture, and AI readiness that most teams were still building when we first published this model. That’s a fact about where most security organizations were in 2024.

The transition from AI-Assisted Operations (Level 2) to Partial Autonomy (Level 3) is primarily a governance problem, not a tooling one. The tools are capable. What most organizations are missing is an understanding of the foundation of data and trust that Partial Autonomy (Level 3) requires, including the role humans play in building it.

When analysts work with AI assistance, they leave traces. Which queries they accept. Which results they act on. Which steps they modify or override. Over time, the system learns which investigation patterns the team trusts, which AI recommendations get acted on, and where analyst expertise is required – the kind of institutional knowledge that only comes from doing the work. Partial Autonomy is built on that record, not installed on top of an existing stack.

The path from AI-Assisted Operations to Partial Autonomy starts earlier than most organizations realize. It begins before they’re thinking about autonomy at all. Every assisted workflow is building toward what comes next.

What Holds Organizations Back

The primary barrier between AI-Assisted Operations (Level 2) and Partial Autonomy (Level 3) is accountability.

Consider how the automotive industry defined its equivalent of Partial Autonomy – SAE Level 3.

The designation applies only within specific, defined operational conditions. Outside those conditions, the human must take control. What qualifies a system for L3 is defined before autonomous operation begins: explicit parameters, a defined scope, and clear conditions for human override. Governance precedes autonomy.

Consider Waymo. It is the most capable autonomous system deployed at scale today — L3+ — operating without a safety driver under defined conditions. The vehicle is remarkable. But Waymo’s primary innovation is the organization built around it: the cloud infrastructure that keeps cars in autonomous condition, the human operations that handle exceptions the system cannot cover. The more autonomous the system, the more organizational maturity it required to build. High autonomy is an organizational capability.

The same logic applies in security operations. Accurate AI is the foundation. What makes Partial Autonomy legitimate is what gets built on top of it: defined rules of engagement, pre-approved policies, audit trails, and a clear organizational answer to who is responsible when an AI verdict is acted on. That accountability sits with the security team. When automation fires, it fires because someone made a deliberate governance decision to allow it. That is what makes it auditable, defensible, and durable.

Gartner’s readiness criteria for AI SOC deployments require that operational workflows be established in playbooks before AI is introduced₄. In practice, the organizations that advanced most consistently treated that requirement as a sequencing discipline, not a box to check. They defined their rules of engagement before turning on automated response.

The second learning was the attacker asymmetry. Defenders who stall between AI-Assisted Operations and Partial Autonomy have often done the validation work. The AI logic checks out. What remains is the decision to extend that trust to autonomous action — and that decision takes time. Attackers move differently. They deploy, observe what works, and iterate. Governance is an externality. Trial and error with no consequences for failure is a significant operational advantage. The gap between a defender’s trust-building timeline and an attacker’s operational tempo is structural. It compounds.

Why High Autonomy Stays on The Horizon. And Why That Matters Less Than We Thought

Eighteen months of deployment have also changed how we think about the upper end of the model.

When the original post was written, High Autonomy (Level 4) was described as dependent on a level of AI reasoning we hadn’t yet seen in production security environments. That remains the right framing. What’s changed is how close that horizon has become. Two years ago, asking a model to reason through a multi-stage attack, correlate signals across data sources, and produce an auditable verdict required significant scaffolding and produced inconsistent results. That’s no longer true. The gap between where AI was and where High Autonomy requires it to be has narrowed substantially.

High Autonomy still requires more than capable models. Institutional trust takes time to build. Accountability structures have to go beyond controlled tests to survive real incidents. Human oversight has to be redefined from reviewing individual actions to governing a system’s behavior within a defined scope. Those are organizational problems, technology doesn’t solve them. That work is already underway at Partial Autonomy (Level 3). What the road to High Autonomy requires is only visible from Partial Autonomy. Organizations that haven’t operated there yet are planning for a destination they haven’t seen. The knowledge of what it takes is path-dependent, and it emerges from operation, not from design.

As organizations move deeper into Partial Autonomy, the distinction between levels matters less in practice. What security leaders actually want is relevant control: governance over the decisions that matter, without being burdened by the ones that don’t. You cannot be responsible or accountable for a system that asks you to review everything.

Control over the right decisions is what matters. An analyst reviewing every alert has maximum control and minimum leverage. A system that acts autonomously on well-understood threat patterns, surfaces only the ambiguous and novel cases for human judgment, and maintains a complete audit trail, gives the analyst control over exactly what deserves their attention. That is a better and more focused version of human oversight.

High Autonomy, seen through this lens, is AI that has earned sufficient trust within a defined scope. The remaining human decisions are the ones that require human judgment, because the governance architecture evolved to allocate human attention correctly.

In the same way, a pilot does not manually adjust every control surface for the duration of a flight. They set the destination, define the parameters, and monitor the instruments. The system handles thousands of micro-corrections that would be impossible to manage directly. The pilot’s job is to govern the conditions under which the aircraft flies itself, not to manage every control input directly. Nobody describes this as a lack of pilot control. It is a better allocation of pilot judgment. And it works because of the environment surrounding the autopilot: pilot training standards, airline operational doctrine, air traffic control, and regulatory frameworks. The technology is one layer of a much larger system.

The governance work done at Partial Autonomy is the same work that produces High Autonomy. Organizations investing in it now are not waiting for a future capability release. They are building the foundation on which High Autonomy operates.

What This Means for the Road Ahead

The first step toward Partial Autonomy is a policy decision. Define the conditions under which your organization will allow a system to act: which response actions, against which threat types, within what scope, under whose authority. Write it down, however rough. That document is the actual starting point. Without it, the tooling is irrelevant.

The work at Partial Autonomy is real, meaningful, and available now. Security teams that define accountability structures before deploying autonomous systems, build a record of AI efficacy in their specific environment, and treat governance as a prerequisite rather than an afterthought, are the ones that reach and sustain Partial Autonomy. They are also the ones best positioned for what comes next. That work produces a more integrated SOC — data, AI, and response operating as a unified system.

High Autonomy remains the north star. This clearly articulated ideal state stops organizations from settling too early. It is the same function that “zero trust” serves as an architectural principle: no organization fully achieves it. Every organization is better for pursuing it.

The tools are capable. The frontier models have advanced significantly since our maturity model was first introduced. The capability gap that once made waiting feel reasonable has narrowed. What remains is the institutional work. That work is always harder than buying a product, which is why vendors who are honest about it are worth paying attention to.

SentinelOne customers operating the Autonomous SOC are seeing it in their numbers: 75% faster investigations, 4x more threats handled, 42% fewer false positives₅. Read the IDC Business Value Snapshot.

References

¹ Gartner, “AI SOC Agents: Harnessing Innovation, Managing Expectations,” Kevin Schmidt, Alex Tytarenko, Steve Santos, 25 February 2026. G00841784.

² Gartner, “AI SOC Agents: Harnessing Innovation, Managing Expectations,” Kevin Schmidt, Alex Tytarenko, Steve Santos, 25 February 2026. G00841784.

³ SAE International, “Taxonomy and Definitions for Terms Related to Driving Automation Systems for On-Road Motor Vehicles,” SAE Standard J3016_202104, April 2021. https://www.sae.org/standards/content/j3016_202104/

⁴ Gartner, “AI SOC Agents: Harnessing Innovation, Managing Expectations,” Kevin Schmidt, Alex Tytarenko, Steve Santos, 25 February 2026. G00841784.

⁵ IDC Business Value Snapshot, “The Business Value of SentinelOne Singularity AI SIEM,” Michelle Abraham and Matthew Marden, May 2026, sponsored by SentinelOne. #US54435826-BVS.

Third-Party Trademark Disclaimer 

All third-party product names, logos, and brands mentioned in this publication are the property of their respective owners and are for identification purposes only. Use of these names, logos, and brands does not imply affiliation, endorsement, sponsorship, or association with the third party.