Security teams have spent decades trying to engineer humans out of the equation—building sophisticated tools and automated workflows to remove the "human error" factor. Yet breaches continue, analysts burn out, and the talent shortage grows more acute.

What if we've been approaching this problem backwards?

Human-centric security flips the paradigm. Instead of treating employees as vulnerabilities to be patched, this approach recognizes human judgment as irreplaceable. Most socially-engineered attacks—business email compromise (BEC), vendor impersonation, credential phishing, account takeover—enter through email and evade traditional defenses. Stopping them requires contextual judgment only humans can provide.

For CISOs, human-centric security transforms overwhelmed responders into empowered defenders.

This article draws from insights shared in the Human-AI SOC webinar series featuring real-world perspectives from security practitioners. Watch the full recording to hear directly from industry experts on implementing these strategies.

Human-centric security is an approach that designs systems, processes, and tools around human behavior and workflows rather than forcing humans to adapt to technology. It moves beyond traditional security awareness training—which often treats employees as problems to be educated—toward a model where technology amplifies human capabilities.

The distinction is critical. Legacy security tools often require analysts to conform to their interfaces, workflows, and alerting logic. Human-centric security inverts this relationship, asking instead: how can our tools make analysts more effective at what humans do best?

This philosophy is captured in a simple principle: AI scales the work, humans seal the judgment. Technology handles the computational heavy lifting—processing millions of logs, correlating events, enriching alerts with context—while humans apply the critical thinking, institutional knowledge, and nuanced decision-making that machines cannot replicate.

As Shira Shirdiga, who leads cyber defense at Abnormal AI, explained during the webinar: "We are not replacing the analyst. We are replacing the toil and elevating the expertise, trust, and the outcomes."

This represents a fundamental shift from viewing humans as "the weakest link" to recognizing them as essential decision-makers. In a human-centric model, AI drafts the context, timelines, and suggestions. Humans decide on actions. The SOC analyst isn't fighting against their tools—they're working alongside intelligent systems designed to support their judgment, not override it.

The operational challenges facing security teams have reached a breaking point. A significant portion of analysts agree that manual processes have increased their burnout, while the vast majority report lacking time for strategic work like threat hunting or professional growth. These aren't abstract concerns—they represent a looming talent crisis.

The human cost of reactive security extends beyond individual burnout. When experienced analysts leave due to exhaustion and frustration, organizations lose institutional knowledge that took years to develop. That senior engineer who understands your environment's quirks, your detection logic's blind spots, and your incident response playbooks' edge cases—they're irreplaceable in the short term and expensive to develop in the long term.

These challenges are not just operational. They're deeply human. Analysts are burning out, career growth is stalling, and the constant firefighting leaves little room for strategic work and advanced threat detection. The never-ending queue of alerts creates a treadmill effect where teams can never get ahead, only respond to what's immediately in front of them.

For board-level conversations, this translates into tangible business risk. Security effectiveness depends directly on analyst retention and engagement. A burned-out team running on empty will miss the subtle indicators of a sophisticated BEC attack or vendor impersonation attempt. A disengaged analyst clicking through alerts mechanically won't catch the credential phishing campaign that doesn't quite fit the pattern. Human-centric security addresses these risks by making the security function sustainable.

When analysts aren't drowning in false positives and low-value alerts, they can apply genuine attention to the signals that matter. Organizations leveraging human-AI collaboration consistently report significantly improved accuracy in detecting threats like BEC, vendor impersonation, and account takeover. Human judgment catches what automation misses—the contextual understanding that an unusual login pattern coincides with a known business trip, or that a seemingly anomalous file transfer aligns with an announced project.

The combination of AI-powered triage and human review creates a detection capability greater than either alone. AI handles the volume, humans handle the nuance.

Perhaps the most striking finding from the research: the overwhelming majority of analysts report higher job satisfaction with a human-centered AI approach. Security professionals didn't enter this field to click through endless alerts—they wanted to protect organizations, investigate threats, and outsmart adversaries. Human-centric tools return those meaningful aspects of the work to them.

Reduced burnout translates directly to lower turnover costs. Recruiting, hiring, and training a new security analyst represents a significant investment. Retaining experienced talent by eliminating the soul-crushing aspects of the job delivers immediate ROI.

With routine triage automated, analysts are pivoting to higher-value activities: threat hunting, proactive security initiatives, and mentorship of junior team members. This shift fundamentally changes the security posture from constantly playing catch-up to actively hunting for threats before they materialize.

Most teams are thinking about moving from more of a reactive defense to a proactive strategy. Human-centric AI makes this transition possible by freeing up the analyst hours required for activities like hypothesis development, detection engineering, and threat research.

The framework that best captures human-centric security implementation is the distinction between copilot and autopilot. In the copilot model, AI handles summarization, context gathering, deduplication, and analytics. It processes the information, identifies patterns, and presents findings. But humans retain decision-making authority on actions.

This isn't about limiting AI capabilities—it's about recognizing where human judgment adds irreplaceable value. Deciding whether to quarantine an endpoint, escalate to executive leadership, or dismiss an alert as benign requires contextual understanding that current AI systems cannot reliably provide.

Abnormal's behavioral AI evaluates every email through three layers of intelligence:

• Identity Aware: Who is this sender? The platform builds profiles from directories, sign-in patterns, and communication histories to verify sender authenticity.

• Context Aware: Is this request normal for this relationship? It maps relationships and analyzes tone, cadence, and frequency of interactions to spot anomalies.

• Risk Aware: Does the content signal suspicious intent? NLP and content analysis identify persuasive language patterns designed to deceive.

This allows detection of attacks that have no malicious payload—just persuasive language designed to manipulate recipients into taking harmful actions.

The practical impact of human-centric AI becomes clear in specific workflow improvements. Alert triage that previously consumed significant time—as analysts switched between multiple log sources and tools—now takes a fraction of that time with AI-powered summarization and context gathering.

The principle here is straightforward: automate the plumbing, not the judgment. Let AI handle the mechanical work of pulling together relevant data, correlating events, and presenting findings. Reserve human attention for the decisions that matter.

Security teams constantly produce SOPs, process documents, and IR-related documentation. AI tools transform this from tedious manual effort into streamlined generation, letting analysts describe what they need and receiving structured output ready for validation and deployment.

Human-centric security doesn't mean blind trust in AI outputs. As practitioners emphasize, AI triage agents are very handy, but you have to be the final decision maker. Validation remains essential throughout the workflow.

When integrating AI and LLMs into security operations, organizations must consider risks including prompt injection, sensitive data disclosures, and model poisoning. OWASP provides frameworks for understanding these risks. Guardrails like data minimization and PII reduction protect against inadvertent exposure while enabling AI's benefits.

Quantifying human-centric security's value starts with operational measurements:

• Triage efficiency: Track reduction in manual triage hours—if your team previously spent the majority of time on alert processing and now spends far less, that's measurable efficiency gained

• Signal quality: Monitor false positive reduction rates as AI-powered filtering improves signal-to-noise ratios

• Coverage expansion: Demonstrate how teams achieve higher coverage and quality without changing headcount

When the same analysts can now monitor additional systems, investigate more thoroughly, or respond to incidents faster, that represents real operational improvement.

Beyond operational efficiency, measure the strategic impact. Analyst retention rates and satisfaction scores indicate whether the human-centric approach is delivering on its promise of sustainable security work. Track time allocated to proactive versus reactive work—this ratio reveals whether your team is escaping the firefighting trap.

Threat hunting metrics matter here too. How many hypotheses is your team developing and testing? What percentage leads to detection improvements? These indicators show whether freed analyst capacity is translating into enhanced security posture.

One practitioner shared a compelling example: an analyst who proactively identified stale AWS accounts and cloud resources delivered a win on both the security posture side and the cost savings side. Human-centric AI enables this kind of value creation by freeing analysts to pursue initiatives beyond immediate alert response.

Notably, the vast majority of surveyed leaders have no plans to reduce headcount due to AI adoption. The financial model isn't about replacing analysts—it's about maximizing the value each analyst delivers. Mapping MITRE ATT&CK TTPs to detections, tuning detection logic, and improving coverage represent high-value activities that directly enhance security while being difficult to outsource or automate.

Implementing human-centric security doesn't require a revolutionary overhaul. Start small and identify your core use cases:

• Begin with AI in shadow mode, validating recommendations against analyst decisions

• Progress to human-approved actions where AI suggests and analysts confirm

• Only after building confidence should you narrow to autonomous actions with rollback capabilities

This staged approach builds trust while managing risk. Each phase provides learning opportunities to refine the human-AI collaboration model for your specific environment.

Human-centric security requires human-centric talent development. Upskilling strategy should focus on creating AI generalists and power users who can leverage these tools effectively. Create AI-driven roles and refine hiring practices to reflect the changing nature of security work.

Enable engineers with AI tools and training. Detection engineering, for example, benefits enormously from AI assistance in enriching detections and tuning logic to reduce false positives and improve coverage gaps.

As AI adoption matures, organizations benefit from dedicated teams managing AI deployment. These teams focus on privacy, compliance, and emerging risks—ensuring that human-centric AI remains trustworthy. Vendor evaluation should prioritize transparency in AI model training, third-party evaluations, and compliance with industry standards.

Human-centric security treats people as partners, not problems. By designing systems that amplify human judgment rather than replace it, organizations build sustainable security programs that can attract and retain talent while improving detection and response capabilities.

The future is already taking shape. Teams implementing human-centric AI are seeing reduced strain without staff cuts, alignment between leadership and analysts on the path forward, and sustainable success that doesn't burn through people like disposable resources.

Ready to see how human-centric AI can transform your security operations? Request a demo to explore how Abnormal's approach keeps humans at the center while automating the work that exhausts them.

• Human-centric security inverts the traditional model by designing tools around analyst workflows rather than forcing humans to adapt to technology—treating people as essential decision-makers instead of vulnerabilities to patch.

• The copilot framework keeps humans in control: AI handles computational tasks like summarization, context gathering, and alert triage while analysts retain authority over actions and decisions that require contextual judgment.

• Organizations implementing human-centric AI security report improved detection accuracy for socially-engineered attacks, higher analyst retention and satisfaction, and a measurable shift from reactive firefighting to proactive threat hunting.

• Successful implementation follows a staged approach—starting with AI in shadow mode, progressing to human-approved actions, and only narrowing to autonomous actions after building trust and validation.