AI has become the loudest buzzword in cybersecurity. Every MDR provider now claims to be “AI-powered,” “AI-driven,” or “AI-enabled.” Demos are filled with automation graphs, flashy dashboards, and promises of lightning-fast response.
But here’s the uncomfortable question most security leaders don’t ask early enough:
Is your MDR provider actually using AI correctly with analysts “on-the-loop”, or are they just automating mistakes at scale?
Because there is a massive difference between:
- AI replacing human judgment, and
- AI amplifying human judgment
One leads to faster failures. The other leads to better security outcomes.
This blog explains what “AI + analyst on-the-loop” really means, why it matters more than pure automation, how many MDR providers get this wrong, and how CyberMSI deliberately built an AI-first, Microsoft-enabled MDR model that balances speed, accuracy, accountability, and trust.
The AI Problem in Modern MDR
Security operations today face three compounding realities:
- Alert volume is exploding across endpoints, identity, cloud, email, SaaS, and now AI agents
- Human analysts are scarce and expensive, and burnout is real
- Attackers are already using AI to move faster, evade detections better, and instantaneously scale attacks
AI in the SOC is not optional anymore. Without it, MDR providers simply cannot scale. Here’s the problem though. Most MDRs are jumping straight from human-intensive to AI-enabled operations, effectively skipping the disciplined approach required to do it accurately and safely.
The results based on real-life examples that we’ve seen in the market thus far include:
- Automated triage of incidents without sufficient business or data context
- Auto-closure of alerts that shouldn’t be closed
- Undue trust in AI’s ability to correctly analyze incidents without adequate mechanisms (e.g. RAG)
- Speed prioritized over correctness
- Analysts reduced to passive observers or removed entirely at many MDR providers
This is not progress; it’s a risk multiplier.
Automation vs. AI + Analyst On-the-Loop: To understand the difference, let’s be precise.
Fully Automated (Analyst-Out-of-the-Loop): In this model:
- AI decides what’s malicious
- AI decides what to contain
- AI executes response actions
- Humans review incidents after the fact, if at all
This approach is attractive to MDR vendors because:
- It reduces staffing costs
- It looks impressive in demos
- It scales cheaply
But it fails in real-world environments where:
- Identity and data context matters
- Business requirements and criticality vary
- False positives have real impact
- Automated containment can break mission-critical or load-bearing systems
- Attackers deliberately manipulate signals
Pure automation assumes the model is always right; this assumption is flawed.
AI + Analyst On-the-Loop: In an AI + analyst on-the-loop model:
- AI agents perform scale well for data-intensive tasks (logs collection, running queries, triage, enrichment, correlation, prioritization, etc.)
- Analysts retain decision authority over containment and escalation
- AI recommendations are reviewable, explainable, and auditable
- Humans intervene before irreversible actions, not after
The analyst is not slowing the system down. The analyst is preventing major mistakes. In effect, this model preserves:
- Speed where threat actors are moving at machine-speed
- Judgment where human experience and context is irreplaceable
- Accountability where response actions matter
This is the model CyberMSI has intentionally built.
Why Pure AI Fails in Security Operations: Security incidents are not binary classification problems because they involve:
- Identity relationships
- Privilege chains
- Business workflows
- Data sensitivity
- Assets criticality
- Regulatory impact
- Operational timing
- Human behavior
AI is exceptional at pattern recognition. It is not exceptional at understanding customer-specific environment and business consequences. Some examples where analyst oversight is essential:
- Disabling an executive account
- Isolating a server supporting revenue-generating applications
- Revoking OAuth tokens tied to third-party integrations
- Blocking cloud API activity that appears malicious but is actually automation
- Responding to AI-agent-driven behavior that mimics users
Without analysts on-the-loop, automation creates new failure modes—often worse than the threats it’s trying to stop.
The CyberMSI Philosophy: AI-First, Not AI-Only
CyberMSI is intentionally AI-first, not AI-exclusive. This distinction matters, so CyberMSI uses AI agents to:
- Ingest and normalize massive cybersecurity telemetry volumes
- Correlate signals across Microsoft Defender XDR, Sentinel, Entra ID, Purview, AWS, Azure, GCP cloud workloads
- Enrich incidents with identity, device, cloud, and data context
- Prioritize incidents based on real-world risk, not just alert severity
- Recommend containment and remediation actions aligned to customer policy
But analysts stay on-the-loop for:
- Incident verdict validation
- Containment approval
- Escalation decisions
- Business-impact judgment
- Customer communication
- Post-incident improvement
AI accelerates the SOC while our analysts ensure we’re correct.
Microsoft-Enabled AI at the Core: CyberMSI’s AI-first MDR is deeply integrated with Microsoft’s security ecosystem, including:
- Microsoft Defender XDR
- Microsoft Sentinel
- Microsoft Entra ID
- Microsoft Purview
- Microsoft Security Copilot
- Azure, AWS and GCP security telemetry
This matters because:
- AI agents operate on native, high-fidelity telemetry
- No duplicated tooling
- No shadow SIEMs
- No proxy dashboards hiding operations
CyberMSI’s AI agents work inside the customer’s Microsoft security stack, preserving:
- Data ownership and custody
- Native response actions
- Auditability of all AI and analyst performed responses
- Operational and KPI transparency
We’ve not bolted our AI agents on top of a SOC; we’ve woven our AI agents into the operational fabric.
Multiple AI Models, Not a Single Point of Failure: Another critical difference: CyberMSI does not rely on a single foundational AI model or agents such as Microsoft Security Copilot. Instead, we use two different AI models optimized for different cybersecurity incident management tasks:
- High-speed classification and triage
- Contextual reasoning and correlation
- Compliance and control validation
- Narrative generation for reporting
Why this matters:
- Different tasks require different AI model strengths
- Cost optimization while preserving performance and scalability
- Reduced model bias and failure risk
- Better accuracy across use cases
- Avoidance of AI vendor lock-in
CyberMSI’s SecOps agents are resilient by design, which is not something many MDR providers can legitimately claim in their case.
Analyst On-the-Loop = Accountability: One of the most overlooked benefits of analyst-on-the-loop is accountability. When AI operates alone:
- Who is responsible for a bad containment action?
- Who explains decisions to auditors?
- Who owns business disruption?
- Who improves the model after mistakes?
When analysts remain on-the-loop:
- Analysis and decisions are attributable
- Incident verdicts and actions are explainable
- Improvements are intentional and targeted
- Trust is preserved at all times
CyberMSI analysts don’t override AI; we actively guide it. This collaboration and supervision is what makes AI-enabled automation safe.
AI + Analysts Enables True End-to-End Incident Management: Many MDRs claim “response,” but stop at recommendations. CyberMSI’s AI + analyst on-the-loop model enables:
- Faster triage without overwhelming analysts
- Higher confidence incident validation
- Approved, executed containment actions
- Coordinated eradication and recovery
- Detection improvements pushed immediately after incidents
AI handles the scale while analysts own the outcomes.
Transparency Is Non-Negotiable: Another sign your MDR is using AI correctly is when you can see everything. As such, CyberMSI operates with:
- Real-time visibility for customers into the incident queue
- Native access to Defender XDR and Sentinel
- No black/gray-box MDR UI
- Full audit logs of AI and analyst actions
AI decisions aren’t hidden, nor analyst decisions are abstracted. We believe that operational transparency builds trust and exposes weak MDRs quickly.
Questions You Should Ask Your MDR Provider: If you’re evaluating or already using an MDR, ask directly:
- Is AI making containment decisions without analyst review?
- Can analysts override AI recommendations?
- Are AI decisions explainable and auditable? How do we access this information in real time?
- How many AI models are used and why?
- Does AI operate inside my tenant or on exported data?
- Who is accountable when automation causes adverse impact?
- Can we see the incident queue in real time?
If the answers are vague, you already know the truth.
Bottom Line: AI is essential to modern SOC, but AI without analyst oversight is operational roulette. The future of effective security operations is not:
- Humans vs machines
- Analysts replaced by automation
It is:
- AI agents handling scale and speed of data-intensive and patterns analysis
- Analysts staying on-the-loop for judgment, transparency, accountability, and trust
CyberMSI was built on this principle from day one. As an AI-first, Microsoft-enabled MDR provider, CyberMSI combines:
- Advanced AI agents
- Deep Microsoft Defender XDR, Sentinel, Entra ID, Purview product security expertise
- Experienced SOC professionals
- Full incident ownership
- Transparent operations
- Data ownership and custody inside the customer tenant at all times
AI-enabled automation matters. Accuracy matters more. Accountability matters most.
If your MDR provider can’t clearly explain how AI and analysts work together before containment happens, you’re not buying modern security; you’re buying marketing hype.
And threat actors are counting on that confusion.
Let’s chat if you’d like to better understand our AI-first, Microsoft-enabled MDR capabilities.