Real World Agentic AI Use Cases In Cybersecurity
Mid-market security leaders face enterprise-grade attackers with a fraction of the staff and budget. Tool sprawl, noisy telemetry, and constant product updates create a fragile stack that already runs hot before the first critical incident hits. Agentic AI arrives in this context, not in a laboratory.
Surveys show that around 18 percent of middle market organizations reported a breach in the last year, with ransomware hitting roughly a quarter of those firms. In the UK, 45 percent of medium-sized businesses experienced cybercrime in the past 12 months, with phishing still the dominant entry point. Breach costs for mid-sized companies now average around 3.5 million dollars per incident. For a lean IT and security group, one mistake can cost a year of budget.
You can see this pressure in recent incidents. The Change Healthcare ransomware attack in 2024 disrupted U.S. healthcare billing nationwide and is projected to cost parent company UnitedHealth more than 2.3 billion dollars in response and recovery, on top of a 22 million dollar ransom payment. MGM Resorts reported over 100 million dollars in impact from its 2023 attack after social engineering of the help desk led to domain-wide ransomware. The National Public Data breach potentially exposed 2.9 billion records in 2024, underscoring how a single compromise can scale far beyond one company.
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The bar chart above highlights three simple facts. Breaches against mid-sized organizations are common, cybercrime against medium businesses remains high, and a single breach can erase years of security investment. For a CISO who cannot simply add fifty analysts, smarter automation is no longer optional.
For many teams, the real constraint is human attention, not tools. A typical SIEM or XDR platform will surface thousands of alerts per day, yet analysts can meaningfully investigate only a small subset. Studies of AI SOC deployments show that teams often must reduce analyst alert handling workload by 70 to 80 percent to regain control of operations. Without that change, important signals remain buried. Guides such as the top threat detection platforms explain how this alert flood developed over time.
Identity-based attacks make the situation worse. Verizon and other studies estimate that roughly 70 percent of breaches now start with stolen or abused credentials. Salt Typhoon campaigns against U.S. telecommunications providers remained undetected for one to two years while adversaries used living off the land techniques and valid accounts to move laterally across networks. The Snowflake breaches in 2024 affected at least 165 organizations using stolen credentials without multi-factor protection. These incidents align directly with MITRE ATT&CK techniques for initial access, credential access, lateral movement, and exfiltration, and expose gaps that traditional alert rules simply miss.
Cloud adoption increases that exposure. The Change Healthcare incident shows how one unprotected remote access point in a cloud-connected environment can stall critical national services. Cloud detection and response research documents that misconfigurations, overly permissive roles, and unsupervised service accounts drive a large portion of modern cloud breaches. Over half of companies report significant cloud security incidents linked to visibility gaps and configuration drift. Resources like the cloud detection and response guide dig into these patterns in more depth.
At the same time, regulatory pressure continues to grow. Mid-market companies must show controls aligned to frameworks such as NIST SP 800-207 for Zero Trust Architecture, while also mapping detections and coverage to MITRE ATT&CK for operational proof. Boards now ask blunt questions: Which ATT&CK tactics are covered and which are gaps? How quickly are high-risk identities isolated after a suspected compromise? Coverage analyzers aligned to MITRE ATT&CK, such as those described in Stellar Cyber’s own materials, exist because auditors and insurers expect quantitative answers.
Against that backdrop, simple playbook automation helps, but it is not enough. It clears individual tasks. It does not run complex investigations, correlate across domains, or adapt as attackers change tradecraft. That is where agentic AI enters the picture. The agentic SOC guides frame this shift as moving from human-triggered scripts to autonomous, goal-oriented digital analysts.
From scripts to agentic AI in security operations
Before exploring specific agentic AI security use cases, we need a clear distinction between classic automation and truly agentic workflows. Many CISOs have been disappointed by tools that promised autonomy but only offered brittle runbooks. Clear definitions prevent the next wave of hype fatigue.
Simple automation executes a fixed sequence of steps when a known trigger occurs. A SIEM rule fires, a SOAR playbook collects some context, perhaps blocks an IP or disables an account. Useful, but static. If the input does not match expected patterns, the automation stalls or fails silently. Human analysts remain responsible for building the narrative and making most decisions.
Agentic AI operates differently. It consists of AI agents that can plan, act, and adapt across multi-step workflows. Given a goal such as “investigate this possible credential theft,” agents decide which data sources to query next, which MITRE ATT&CK techniques may apply, what additional evidence is needed, and which response options best match policy and risk appetite. They can read raw events, call APIs, update tickets, and call other agents in a chain.
Simple automation compared to agentic workflows and human analysts
This comparison reflects what we see in practice. Simple automation removes some repetitive keystrokes, but still expects an analyst to stitch a full picture. Human analysts have judgment, but only so much time. Agentic AI workflows sit in the middle: they act like tireless junior analysts who can run entire investigations on their own, then escalate well-structured cases with evidence, ATT&CK mapping, and recommended responses.
If you read the latest AI SOC architecture guide, you will notice a common pattern. Agentic AI does not replace a SIEM or XDR. It sits above them, orchestrating data, correlating alerts, and running continuous investigations. That distinction matters for budget planning and for explaining the strategy to your board.
Core agentic AI security use cases that matter most
Cross-domain threat detection and prevention
Most serious attacks now span endpoints, networks, cloud, email, and identity. Traditional tools see only slices of that story. A failed admin login here, a DNS anomaly there, maybe an unusual S3 API call. No single system has enough context to declare an incident with confidence.
National Public Data, Salt Typhoon, and the Snowflake breaches all demonstrated this fragmentation. Attackers combined credential theft, living off the land techniques, and cloud access to quietly stage and exfiltrate massive datasets. Each step on its own looked almost normal. Only a cross-domain view of behavior revealed the pattern.
Agentic AI in security operations addresses this by assigning different agents to focus on specific data planes: one watches network flows, another endpoint EDR logs, another cloud audit events, and another identity and access telemetry. Correlation agents then assemble relationships between entities, map actions to ATT&CK techniques, and build kill chain timelines that show how a suspicious process on an endpoint connects to an unusual identity pivot in Azure and odd database queries in Snowflake.
This directly supports Zero Trust ambitions from NIST SP 800-207. That document stresses continuous verification and context-aware policy enforcement rather than implicit trust based on network location. Agentic detection agents provide the continuous behavioral assessment that policy engines need to make more precise allow, challenge, or deny decisions in real time.
Resources describing the XDR Kill Chain approach outline how kill chain-aligned analytics help teams see multi-stage attacks earlier and in a more structured way. Agentic AI essentially automates kill chain interpretation across all your telemetry.
Automated incident investigation and response workflows
Investigation, not detection, often dominates analyst time. After a high-severity alert, someone must consolidate evidence, check similar entities, consult threat intelligence, and draft a response plan. For complex incidents such as Change Healthcare or MGM, these steps consumed days. During that time, systems stayed degraded, and executives lacked clarity.
Agentic AI systems change this pattern by running end-to-end investigations autonomously. When an initial signal crosses a certain risk threshold, a case analysis agent gathers all related alerts and telemetry, identifies affected entities, and summarizes the probable root cause along with the ATT&CK tactics involved. Other agents check for spread: similar activity on sibling hosts, other use of the same credential, connections to known malicious infrastructure from threat intelligence feeds.
Once sufficient evidence exists, response-oriented agents propose options that align with policy. For example, isolate a host, disable a token, move a user into a restricted group, or enforce step-up authentication. In more mature deployments, agents can execute contained response actions directly for well-defined patterns, while routing ambiguous situations to human analysts. This “human on the loop” model reflects both security best practice and current regulatory expectations.
Stellar Cyber’s 6.2 release, for instance, highlights how agentic case analysis and automated narrative generation can reduce time to understanding from days to minutes. Similar principles apply across the market, especially where threat detection, investigation, and response platforms sit at the center of operations.
SOC alert triage and prioritization for lean teams
Alert fatigue remains perhaps the most painful SOC problem. Many mid-market teams still manually open each high or critical alert, only to discover noisy false positives or incomplete context. Analysts burn out, and real attacks slip through at 2 a.m.
Modern incident reports emphasize this gap. AI-driven phishing attacks rose by more than 700 percent between 2024 and 2025, while ransomware incidents climbed over 100 percent in the same period. No human team can manually triage every suspicious email, log line, and endpoint anomaly that these campaigns generate.
Agentic triage agents continuously evaluate new alerts as they arrive, not just on rule severity, but on context: entity criticality, blast radius, past behavior, current campaigns, and ATT&CK technique combinations. Low context alerts about low-value assets may get auto-closed after quick checks. High-risk combinations, such as a privileged account signing in from a new geography while creating new cloud keys, receive instant promotion and a full investigation.
Real-world deployments report that such systems can compress thousands of raw alerts into hundreds of cases per day, often cutting analyst manual triage volume by an order of magnitude while improving detection quality. That frees senior staff to focus on threat hunting, purple teaming, and architecture hardening. The agentic SOC platform overview explains several of these triage patterns in more depth.
Cloud security management and misconfiguration remediation
Cloud misconfigurations remain a leading cause of breaches. Public buckets, over-granted roles, forgotten test environments, and stale service accounts create a soft target surface. The Snowflake and Change Healthcare incidents both highlight the risk of credential and configuration weaknesses in cloud-connected systems.
Traditional cloud security posture management tools identify issues, but often hand security teams large static lists. Fixing them at scale requires coordination across DevOps, application owners, and compliance staff. In practice, many findings linger for months.
Agentic AI brings continuous, context-aware monitoring to cloud security management. Specialized agents watch configuration drift, identity changes, and workload behavior against baselines. When an S3 bucket suddenly becomes public or a service account gains new, powerful roles, an agent can immediately flag the change, assess business criticality, and propose or execute safe remediation such as rolling back to the previous policy or attaching a known good template.
For KMS keys, IAM policies, or Kubernetes clusters, agents can simulate proposed changes before applying them, checking for breakage risks. When combined with policy definitions rooted in NIST SP 800-207 Zero Trust principles, this creates a feedback loop in which cloud posture stays much closer to design intent. Mid-market teams that cannot field a dedicated cloud security squad gain practical enforcement power.
The cloud detection and response overview goes deeper into how continuous analytics across cloud control planes and data planes reveal attack chains that static scanners miss. Agentic workflows sit on top of that visibility to turn findings into action.
Identity and access governance with privilege misuse detection
Identity has become the new perimeter. The MGM attack, the massive credential leaks in 2025, and the Snowflake incidents all involved attackers moving with valid credentials rather than obvious malware. Insider threat studies suggest that nearly 60 percent of breaches now involve insiders or compromised accounts.
Classic identity and access governance processes often run quarterly or yearly. Entitlement reviews, role mining, and ad hoc privilege audits help, but do little against an attacker who abuses one account for nine days in a row. The 2024 Salt Typhoon campaign showed exactly this issue, maintaining long-term access inside telecom networks with legitimate-looking credentials.
Agentic AI supports identity and access governance in two ways. First, continuous behavior analytics agents monitor how each identity usually works: which applications it touches, typical data volume, usual geographies, and normal time of day. If an account suddenly pulls gigabytes of data at 3 a.m. from a new region, agents can flag or even suspend the session, regardless of whether MFA was used.
Second, governance-focused agents scan entitlement graphs to find toxic combinations of roles, orphaned accounts, and excessive privileges, presenting owners with prioritized, context-rich recommendations to remove risk. Cases like the MGM breach, where social engineering yielded administrative access, illustrate why such privilege reviews must be continuous, not episodic.
Modern identity threat detection and response material outlines how this blends classical IAM with detection engineering for ATT&CK techniques like Valid Accounts, Privilege Escalation, and Lateral Movement. Agentic systems automate much of that engineering and day-to-day monitoring.
Continuous compliance checks and policy enforcement
Compliance for mid-market organizations has always been resource-heavy. PCI DSS, HIPAA, GDPR, sector-specific mandates, and now executive orders around software supply chain security all require continuous evidence. Yet many firms still treat compliance as a quarterly rush of spreadsheets and screenshots.
NIST SP 800-207 frames Zero Trust as a continuous process that must adapt to changes in assets, threats, and user behavior. MITRE ATT&CK-driven coverage analysis tools show where controls align with real adversary techniques, highlighting blind spots. Both frameworks implicitly call for automation and continuous validation. Humans alone cannot keep pace.
Agentic AI aligns well with this requirement. Policy agents can encode rules such as “all privileged identities must require phishing-resistant MFA” or “no business unit may expose databases directly to the internet.” Other agents then continuously check relevant telemetry, configuration states, and identity records against those policies, opening or updating findings when violations appear.
This moves compliance from point-in-time attestation toward living evidence. For a security architect presenting to the board, showing an ATT&CK coverage heatmap generated daily, coupled with automated policy compliance scores, carries far more weight than a stale once-per-year assessment. The MITRE ATT&CK coverage analyzer materials illustrate how such visualizations support both security and insurance negotiations.
Autonomous threat hunting using cross-domain data
Most mid-market teams aspire to perform threat hunting. Very few can sustain it. Analysts barely keep up with inbound alerts; structured hunts drop to the bottom of the queue. Yet recent breaches, from Salt Typhoon to Change Healthcare, reveal that proactive hunting might have spotted anomalies long before full impact.
Agentic AI threat hunting agents invert this equation. Instead of waiting for alerts, they generate and test hypotheses based on ATT&CK techniques and threat intelligence. For example, an agent might search for signs of credential dumping or unusual remote administrative tool usage across all endpoints, then pivot into network logs and cloud audit trails.
Because agents can run continuously and at machine speed, they explore far more hypotheses than any human team. When they find suspicious patterns, they open cases with ready-made context, mapping suspected techniques, entities involved, and suggested next steps. Over time, analyst feedback trains these agents on which hunts produced value, refining future efforts.
The cyber threat intelligence overview describes how structured ATT&CK mapping enables systematic hunting across the attack lifecycle. Agentic systems simply automate that structured approach and tie it into your existing telemetry stack.
Architectural patterns pairing agentic AI with XDR and SIEM
Even the best agentic AI security solutions will fail if bolted on haphazardly. For a CISO guiding a mid-market organization, the key question is not just “what can agents do,” but “how do they integrate with my current SIEM, XDR, and hyperautomation investments without blowing up risk or budget?”
Most successful designs share several traits. First, they treat Open XDR or a similar data fabric as the foundation. That layer normalizes telemetry across endpoints, network, cloud, identity, and SaaS applications. Agentic AI agents then consume this normalized stream rather than trying to integrate separately with every tool. This reduces integration risk and keeps onboarding of new data sources straightforward.
Second, they integrate with the SIEM rather than replace it outright. Legacy SIEMs still handle compliance logging, long-term retention, and some correlation. Agentic AI and modern XDR platforms sit beside them, taking over real-time detection, multi-domain correlation, and response orchestration. Many organizations start by mirroring logs into an Open XDR platform, letting agents operate on that copy before rethinking SIEM renewal cycles.
Third, response actions are wired through existing hyperautomation stacks and SOAR platforms. Rather than bypassing established change control practices, agentic AI agents call approved playbooks and workflows, just with smarter triggers and richer context. This aligns with governance principles in NIST SP 800-207, which emphasize policy-driven control over network and resource access.
Finally, human oversight remains central. Press releases about human augmented autonomous SOCs stress that agents triage, correlate, and propose, while humans validate high-impact actions and adjust strategy. This model satisfies both security culture expectations and emerging AI governance requirements.
For leaders planning this transition, high-level AI SOC references such as the AI SOC architecture guide and the best AI SOC platforms overview provide practical evaluation criteria. Pay particular attention to how each platform maps detections to MITRE ATT&CK, exposes Zero Trust relevant context, and measures analyst workload reductions in real numbers.
Practical adoption path for mid-market CISOs
Even if the value is clear, adopting agentic AI can feel risky. Concerns range from false positives disrupting business to AI systems acting outside policy. Those worries are valid, especially in regulated industries or environments with fragile legacy applications. The answer lies in staged deployment with clear guardrails.
A pragmatic path starts with read-only deployments focused on visibility and triage. Enable agents to score alerts, build cases, and propose responses, but require human approval for any action that changes systems. Measure changes in mean time to detect, mean time to respond, and analyst time spent per case. If you do not see meaningful gains within a few months, adjust the configuration or reconsider vendors.
Next, identify a narrow, high-volume but low-risk domain for partial autonomy, such as phishing email remediation or isolation of non-critical lab endpoints. Many organizations already trust SOAR playbooks in these areas; agentic AI simply decides when to run them. Monitor error rates, rollback frequency, and user complaints.
Only after these pilots prove safe should teams consider granting broader autonomous authority, particularly around identity controls and cloud configuration rollback. Even then, align every autonomous action type with explicit policy, business owner approval, and logging structures that allow for later forensic review.
Throughout, keep mapping progress against MITRE ATT&CK and NIST SP 800-207. Use coverage analyzers and Zero Trust assessments to show which attack techniques and policy controls now receive continuous, agent-driven attention. Tie each advance to a real breach example that would have been detected sooner or contained more quickly. Executives respond to concrete scenarios: “This setup would likely have caught a Change Healthcare style credential misuse within hours, not days.”
For a deeper study of specific building blocks, resources such as the user and entity behavior analytics guide and the identity threat detection overview provide focused context on behavior analytics and identity-centric controls. Combined with Open XDR and an agentic SOC fabric, they define a realistic path from today’s strained operations to a more autonomous, resilient posture fit for mid-market constraints.