Dow's AI-Enhanced CSOC: Accelerating Security Operations with Generative AI

Dow’s security team has quietly rewritten the playbook for a 125‑year‑old materials science giant by folding generative AI into daily operations — not as a flashy headline, but as a force multiplier that shortens investigation times, elevates junior analysts, and reshapes incident response workflows across the enterprise.

Background​

Founded in the late 19th century, Dow has long been synonymous with industrial chemistry and large‑scale manufacturing. Over the past decade the company’s strategic focus shifted toward sustainability, materials innovation, and global digital transformation. As these efforts expanded, so did the volume and sensitivity of Dow’s digital assets — product designs, manufacturing telemetry, supplier data, and customer contracts — increasing the attack surface and elevating cybersecurity to board‑level concern.
In response, Dow consolidated and modernized its security program under a centralized leadership structure and invested in automation, observability, and AI‑enabled tooling. The company’s security organization, led by Chief Information Security Officer Mario Ferket, adopted an explicit strategy: use AI to augment human defenders, streamline repetitive processes, and make security operations both faster and more accessible to a diverse workforce.
This report unpacks how Dow integrated AI into its Cyber Security Operations Center (CSOC), the operational wins and limits observed to date, how governance and responsible AI practices were embedded early, and what other enterprise security teams can learn when deploying AI in production.

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Why AI, why now: the operational driver​

Modern SOCs face two concurrent pressures: skyrocketing signal volume and chronic talent shortages. Telemetry from endpoints, identity systems, cloud workloads, and network devices produces millions of events daily; triage and correlation are labor‑intensive. At the same time, hiring and retaining experienced incident responders is expensive and slow.
Dow’s approach reframes AI as a tactical solution to two specific operational problems:

• Reduce manual data gathering and ticket enrichment so analysts spend less time aggregating context and more time investigating root causes.
• Lower the bar for junior analysts and cross‑disciplinary apprentices by providing natural‑language interfaces to build queries, synthesize intelligence, and generate repeatable playbooks.

This is not theoretical: the CSOC adopted Microsoft Security Copilot and Microsoft 365 Copilot — deployed alongside other Microsoft Defender and Sentinel capabilities — to automate enrichment, produce incident summaries, accelerate threat hunting, and generate detection queries on demand. The goal is not to replace analysts but to amplify human judgment and velocity.

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Overview of the implementation​

From design partnership to day‑to‑day tool​

Dow’s AI adoption began as a design partnership with its vendor. That collaborative model allowed the CSOC to pilot workflows, iterate on integrations, and surface real user needs before broad rollout. Key implementation choices included:

• Embedding Copilot capabilities into incident triage dashboards so enrichment occurs when alerts fire.
• Connecting Copilot to intelligence feeds and telemetry (endpoint, email, identity) so its outputs are grounded in the organization’s data.
• Using natural language prompts to generate KQL (Kusto Query Language) queries for hunting and forensics, reducing the requirement for deep query‑writing expertise.
• Automating routine mitigation playbooks where appropriate, while retaining human approval for high‑impact actions.

These design decisions were selected to reduce noise, accelerate correlation, and allow analysts to operate at a higher level of abstraction.

Governance: responsible AI and acceptable use​

Dow didn’t treat AI as purely a security problem — it established a cross‑functional responsible AI team that includes Enterprise Data & Analytics, Legal, Privacy, and Security. That team produced:

• A set of responsible AI principles tailored to business and compliance needs.
• An acceptable use policy for generative AI across the enterprise.
• A risk assessment framework to identify where AI might introduce confidentiality or data‑integrity risks (for example, prompt leakage in generative systems).

Embedding governance at the outset helped the organization move fast without tripping regulatory or privacy landmines. Where tradeoffs exist — convenience versus data exposure — the team developed compensating controls such as data‑loss prevention and prompt auditing.

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What Security Copilot delivers in practice​

Ticket enrichment and incident summarization​

One of the most immediate and visible gains at Dow has been automation of alert enrichment and incident summarization. When an alert fires, the Copilot integration:

• Pulls indicators (IP addresses, hashes, domains) from connected intelligence feeds.
• Correlates those indicators with internal telemetry (endpoint detections, mailbox logs, identity activity).
• Produces a concise natural‑language summary of the incident and suggested next steps for investigators.

This replaces manual correlation across consoles and cut-and‑paste sourcing from multiple tools — work that previously consumed significant analyst time.

Threat hunting augmentation​

Threat hunting benefits from Copilot’s ability to generate and iterate on queries. Analysts can:

• Describe a suspicious pattern in plain English.
• Receive a KQL query tailored to Dow’s telemetry schema.
• Refine the query interactively until it produces actionable results.

This capability shortens the hunt‑to‑response cycle and reduces dependence on a handful of senior query authors.

Apprenticeship and talent enablement​

A notable organizational innovation has been using Security Copilot as a virtual mentor within Dow’s CSOC apprentice program. Apprentices — many with non‑IT backgrounds — traditionally required extensive job‑shadowing and months of on‑the‑job training to reach productivity.
With AI assistance they can:

• Ask natural‑language questions about incident data and receive contextualized explanations.
• Learn query construction through ‘show‑me‑how’ examples generated from real telemetry.
• Receive suggested triage actions and playbook steps tailored to the organization’s tooling.

Beyond speed, this model broadens the talent pipeline by enabling nontraditional hires to contribute faster.

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Measurable outcomes and independent evidence​

Dow reports improvements in investigation speed and analyst productivity after integrating Copilot, including reduced time spent on manual enrichment and faster escalation decisions. These claims align with broader industry and research findings showing material gains from generative AI adoption in SOCs.
Independent analyses and vendor‑published research indicate that Copilot‑style tools can yield meaningful productivity improvements in security operations. Studies of live operational telemetry across multiple organizations have documented reductions in mean time to resolve (MTTR) following Copilot adoption, with some analyses reporting improvements on the order of thirty percent three months after deployment. Early adopter case studies have also described time savings in repetitive investigation tasks and substantial gains in junior analyst ramp‑up.
It is important to note that the precise magnitude of gains varies by environment, telemetry richness, implementation scope, and measurement methodology. The largest claims typically come from controlled studies or vendor‑assisted research; organizations should calibrate expectations to their own telemetry maturity and use cases.

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Strengths: where this approach shines​

• Time savings on low‑value tasks. Automating enrichment and summarization frees analysts to focus on investigations that require human judgment.
• Lowered skill barrier. Natural‑language query generation and interactive assistance democratize tasks like KQL query building and evidence correlation.
• Faster threat hunting. Rapid, iterative query generation reduces hunt cycles and helps teams discover lateral movement or anomalous relationships faster.
• Talent scalability. Apprentices and junior analysts achieve baseline productivity faster, which helps bridge shortages in experienced security staff.
• Iterative risk controls. Incorporating a responsible AI team early enables the organization to codify acceptable use and balance utility with data protection.

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Risks and limitations: what to watch for​

Data exposure and prompt leakage​

Generative systems can unintentionally surface sensitive data if prompts or model outputs are not adequately controlled. Enterprises must guard against prompt leakage (where internal data appears in model outputs), and ensure prompt logs do not expose secrets. Dow’s cross‑functional governance mitigates these risks, but organizations with less mature controls remain vulnerable.

Hallucinations and accuracy limits​

AI models can produce plausible but incorrect outputs — a phenomenon known as hallucination. When Copilot generates a KQL query or synthesizes a summary, analysts must validate results against raw telemetry. Overreliance without verification could lead to misattribution or incorrect containment actions.

Overautomation and alert suppression​

Automation can inadvertently dampen analyst intuition. For example, overly aggressive auto‑triage rules or misplaced trust in model prioritization might suppress low‑signal alerts that would have revealed novel attack patterns. Maintaining a human‑in‑the‑loop for strategic containment decisions is critical.

Vendor and ecosystem lock‑in​

Deep integration with a single vendor’s AI stack increases coupling between security workflows and that vendor’s data models, telemetry connectors, and update cadence. Organizations should plan for portability and maintain the ability to extract playbooks and data when vendor architectures evolve.

Adversarial use of AI​

As defenders adopt AI, adversaries do the same. Threat actors can leverage generative models to craft more convincing phishing, write automated malware scripts, or probe detection rules at scale. Security teams must treat AI as both a tool and a threat vector.

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How Dow mitigates risk: practical controls​

Dow’s approach demonstrates a layered mitigation strategy that other enterprises can emulate:

• Establish cross‑functional responsible AI governance before broad adoption.
• Apply principle of least privilege to data accessible by AI tools; do not expose the entire telemetry lake to any single model without controls.
• Implement prompt and output auditing so every Copilot interaction is logged and reviewable.
• Use data‑loss prevention (DLP) and information protection wrappers around AI interfaces to reduce leakage risk.
• Maintain human review for high‑impact containment actions and use automation for enrichment and routine responses.
• Incorporate adversarial testing and red teaming to evaluate how AI changes the attacker/defender dynamics.

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Future directions Dow is pursuing​

The interview with Dow’s security leadership highlighted several forward‑looking priorities that reflect broader industry trajectories:

• Advanced anomaly detection at scale. Applying large‑scale models to detect subtle telemetry patterns and complex multi‑stage intrusions across millions of signals.
• Intelligent rule management. Using AI to recommend tuning, retirement, or consolidation of detection rules, reducing manual rule churn.
• Dynamic alert prioritization. Enriching triage with contextual signals and threat intelligence to dynamically reprioritize alerts based on probable impact.
• AI‑driven playbook optimization. Continuously refining response playbooks based on post‑incident outcomes and reinforcement learning signals.
• Continuous red teaming for AI. Simulating adversarial AI at scale to surface novel attack techniques and model vulnerabilities.

These directions emphasize automation for scale, but with an explicit caveat: humans remain responsible for strategic choices and oversight.

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Lessons for other security teams: a practical checklist​

• Start with use cases, not tools. Pick a narrow set of tasks (enrichment, summarization, query generation) and measure baseline performance before AI.
• Build governance early. Create acceptable‑use policies, data handling rules, and a cross‑functional responsible AI council.
• Protect prompts and outputs. Log interactions, apply DLP, and ensure models do not retain or expose sensitive information.
• Maintain human oversight. Automate low‑risk, repeatable tasks and keep humans in the loop for containment and escalation decisions.
• Measure impact. Track MTTR, analyst time saved, false positive/negative rates, and apprentice ramp times to validate ROI.
• Red‑team your AI. Test the model’s behavior under adversarial conditions and tune guardrails accordingly.
• Invest in telemetry quality. The effectiveness of AI is directly tied to the breadth, fidelity, and accessibility of your telemetry.
• Plan for portability. Keep playbooks and detection logic exportable to avoid vendor lock‑in.

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Critical analysis: balancing enthusiasm with realism​

The integration of generative AI into enterprise security is a major inflection point, and Dow’s experience provides a pragmatic template: targeted adoption, governance up front, and a focus on augmenting human workflows. The operational benefits — time saved on enrichment, faster query generation, and accelerated apprenticeship — are real and corroborated by broader industry studies showing measurable MTTR reductions.
However, caution is warranted. The most headline‑worthy claims often come from vendor partners or carefully selected early adopters. While independent research corroborates meaningful productivity gains, the results are sensitive to selection biases and measurement scope. Reported reductions in MTTR and time savings should be interpreted as achievable under the right conditions — plentiful telemetry, disciplined governance, and iterative implementation — rather than universal guarantees.
Moreover, the threat landscape evolves. AI augments defenders but also arms attackers. The next wave of threats will likely exploit AI to automate reconnaissance, craft bespoke social engineering campaigns, and accelerate exploitation testing. Organizations must treat AI as a double‑edged sword and invest both in defensive AI tooling and in the people and processes that keep models safe and verifiable.

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Practical roadmap: a phased approach to deployment​

• Pilot: Select 2–3 specific, measurable use cases (alert enrichment, incident summarization, query generation). Run a small pilot with a design‑partner model.
• Govern: Stand up a responsible AI committee and draft acceptable use and data handling policies tied to compliance requirements.
• Integrate: Connect AI capabilities to telemetry and intelligence feeds; ensure least privilege for data access.
• Measure: Establish baseline KPIs (MTTR, analyst time per incident, apprentice ramp time) and monitor improvements.
• Iterate: Use feedback loops to tune prompts, playbooks, and automations. Red‑team the system periodically.
• Scale: Expand to additional use cases (rule optimization, dynamic prioritization) while maintaining oversight and auditability.

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Conclusion​

Dow’s pragmatic adoption of generative AI within its security operations illustrates how a legacy industrial firm can responsibly harness cutting‑edge tools to modernize defenses. By pairing a targeted implementation of Security Copilot with a strong governance framework and a focus on talent enablement, Dow achieved operational improvements without sacrificing control.
The broader lesson for enterprise defenders is clear: AI can shift the balance in favor of defenders — but only when it is embedded thoughtfully, measured rigorously, and governed transparently. Security teams that pair technical adoption with organizational safeguards will not only reduce time and toil; they will preserve trust, protect critical data, and be better prepared for the next wave of AI‑driven threats.

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Source: Microsoft Dow's 125-year legacy: Innovating with AI to secure a long future | Microsoft Security Blog