MAIA Unique Capability — Deep Dive

The Adaptive Intelligence
Engine

A self-learning, continuously evolving security intelligence platform that orchestrates multiple specialised AI capabilities simultaneously — sharing context, building institutional memory, and growing smarter with every interaction across your infrastructure.

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The Fundamental Difference

Intelligence That Learns Your Infrastructure — Not a Generic Template

Every security platform claims to be "intelligent." Most apply the same static models, the same generic signatures, and the same rule-based logic to every customer's infrastructure. The result is a security posture calibrated to nobody's specific reality.

MAIA's Adaptive Intelligence Engine is fundamentally different. Rather than applying pre-configured models to your environment, it builds a precise, living understanding of your specific infrastructure — every system, every user, every data flow, every process — and continuously refines that understanding as your environment evolves.

Why This Matters for Financial Institutions

A Tier 1 bank's normal operating behaviour is radically different from a fintech startup's. A settlement system's expected activity patterns at 02:00 are completely different from those at 14:00. A trader's typical data access profile differs fundamentally from a compliance officer's.

Generic security models cannot distinguish signal from noise in this complexity. MAIA can — because it has learned, specifically and precisely, what normal looks like in your environment. And it knows instantly when something deviates.

Digital neural network representing MAIA's adaptive intelligence architecture

Architecture

Multi-Model Orchestration with Shared Context

MAIA is not a single AI model with a fixed context window. It is an orchestrated intelligence platform — multiple specialised capabilities working in concert, each domain expert in its own focus area, all sharing a unified contextual understanding of your infrastructure.

MAIA Intelligence Architecture

MAIA Intelligence Core + Shared Memory
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Behavioural Analyst

Baseline modelling & anomaly detection across all entities

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Correlation Engine

Cross-system signal fusion & pattern recognition

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Threat Intelligence

CVE database, attack pattern & dark web signal integration

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Identity & Access

User entity behaviour analytics & privilege monitoring

☁️

Cloud & Hybrid

Multi-cloud workload & container security intelligence

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Network Intelligence

Traffic analysis, lateral movement & C2 detection

All specialised capabilities share context through a unified intelligence fabric — not siloed, isolated models

Shared Contextual Memory

When MAIA's network intelligence module detects an unusual outbound connection, that signal immediately becomes available to the identity module, the behavioural analyst, and the threat intelligence engine simultaneously. Each module contributes its own domain expertise to the shared assessment. The result is a richer, more accurate picture of whether the signal represents a genuine threat — in real time, not after manual correlation hours later.

350+ Specialised Monitoring Agents

The Intelligence Engine deploys over 350 specialised monitoring agents across your infrastructure — each focused on a specific domain, protocol, system type, or behavioural pattern. Unlike generic monitoring rules that apply identically to all environments, these agents are continuously calibrated to your specific infrastructure, adapting their sensitivity thresholds and baseline expectations as your environment naturally evolves.

Adaptive Learning

How Baselines Are Built, Maintained & Evolved

MAIA's behavioural baselines are not static snapshots. They are living, continuously updated models that reflect the current reality of your infrastructure — automatically distinguishing between a genuine threat deviation and a legitimate operational change.

Initial Discovery & Observation (Week 1–2)

MAIA begins by comprehensively mapping your infrastructure — every system, user account, application, data flow, and network path. Initial behavioural observations are collected across all dimensions simultaneously: process activity, network traffic, file system access, authentication events, API calls, and privileged account usage. This creates the raw material from which baselines are constructed.

Statistical Baseline Construction (Week 2–4)

From the observed data, MAIA constructs multi-dimensional behavioural baselines — not simple threshold rules, but rich statistical models that capture normal behaviour across different time periods (business hours vs. off-hours), different user roles, different system types, and different operational contexts. These baselines account for the natural variability of your environment without being fooled by it.

Context-Aware Calibration (Month 2)

MAIA begins cross-correlating its domain-specific baselines to understand how different parts of your infrastructure interact under normal conditions. It learns that a batch processing job on Server A consistently causes elevated database query volumes on Server B every Sunday at 03:00. When this happens again, it is not flagged as an anomaly. When it happens unexpectedly on a Tuesday at 14:00, MAIA notices immediately.

Continuous Adaptive Refinement (Ongoing)

As your infrastructure evolves — new systems deployed, staff changes, business process modifications — MAIA continuously updates its baselines to reflect the new normal. When your security team explicitly approves a system change, MAIA incorporates it as legitimate context. When a change is unauthorised and unexpected, MAIA flags it instantly. Legitimate operational evolution never triggers false alarms; genuine threats never hide in the noise of change.

Analyst Feedback Integration (Continuous)

Every analyst decision — confirming a threat, dismissing a false positive, escalating an incident — feeds directly back into MAIA's models. Confirmed threats sharpen detection sensitivity for similar patterns. Dismissed false positives refine baseline calibration. Over months and years, MAIA's accuracy in your specific environment becomes a compounding institutional advantage that no adversary can easily replicate.

Reasoning Methodology

Neurosymbolic Reasoning: Logic Plus Learning

Pure statistical machine learning produces predictions without explanations. Pure rule-based logic cannot learn. MAIA combines both — delivering the pattern-recognition power of learning systems with the explainability and precision of logical reasoning.

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Statistical Learning

Detects complex, multi-dimensional patterns across vast volumes of behavioural data that no rule set could anticipate. Identifies subtle correlations and deviations that are statistically improbable — even when no single data point exceeds a simple threshold.

⚖️

Logical Reasoning

Applies structured reasoning to confirm, contextualise, and explain detected anomalies. Evaluates candidate threats against known attack patterns, regulatory frameworks, and institutional policies. Produces human-interpretable explanations for every decision.

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Explainable Outputs

Every alert, every automated action, every risk score is accompanied by a human-readable explanation of precisely why MAIA reached that conclusion. Regulators, auditors, and security analysts receive full transparency — no black-box opacity, no unexplained recommendations.

🎯

Precision & Recall

The combination of statistical learning and logical reasoning achieves a 0.005% hybrid error rate in production deployments. False positives are dramatically reduced without sacrificing sensitivity to genuine threats — eliminating the false dilemma between security and operational efficiency.

Institutional Memory

Security Intelligence That Compounds Over Time

MAIA's intelligence does not reset at the end of each conversation or session. It builds a persistent, accumulating body of institutional security knowledge — specific to your organisation, your infrastructure, and your threat landscape — that grows in depth and accuracy indefinitely.

TB+
Institutional security intelligence per organisation
24/7
Continuous learning and refinement — no downtime
Years
Memory horizon — context that compounds over time
0
Context lost between sessions or updates
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Cross-Session Threat Continuity

MAIA tracks threat actor behaviour across days, weeks, and months — recognising the same adversary's tactics even when they change their tools, infrastructure, and timing. Multi-stage attacks that unfold over extended time horizons are visible as a coherent campaign, not as isolated, disconnected events.

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Compounding Accuracy

Each confirmed threat, each resolved incident, each analyst decision refines MAIA's models further. After 12 months in your environment, MAIA's detection accuracy is materially superior to month one — a compounding accuracy advantage that is unique to your institution and that no competitor can purchase or replicate.

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Regulatory Audit Continuity

MAIA's persistent memory provides a complete, searchable record of every security event, alert, investigation, and response action across your entire history with the platform. Regulatory examinations that might take weeks of manual evidence gathering can be satisfied in hours with MAIA's continuous audit trail.

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Threat Landscape Evolution Tracking

MAIA continuously ingests global threat intelligence — new CVEs, newly observed attack patterns, emerging adversary techniques — and cross-references them against its institutional knowledge of your specific infrastructure. Vulnerabilities relevant to your environment are immediately assessed in the context of your actual security posture, not generic industry averages.

Real-World Application

Adaptive Intelligence in Action

How MAIA's Adaptive Intelligence Engine detects threats that traditional systems miss entirely — illustrated through representative banking and financial infrastructure scenarios.

Scenario 1 — Advanced Persistent Threat

Slow-Burn Credential Theft Campaign

An adversary compromises a low-privilege service account and spends six weeks conducting slow, careful reconnaissance — querying directories, mapping systems, and testing access boundaries — all within individually plausible activity volumes. Traditional tools see thousands of benign-looking events. MAIA sees a coherent pattern of escalating reconnaissance across six weeks of institutional memory, identifies the anomaly before the adversary reaches high-value assets, and triggers a containment response.

Outcome: Detected at reconnaissance stage — 6 weeks before credential escalation
Scenario 2 — Insider Threat

Privileged Account Data Exfiltration Preparation

A senior analyst with full data access rights begins, over a three-week period, querying and accessing client account records at volumes and in combinations that exceed their established behavioural baseline — but individually remain within technically authorised limits. No rule is breached. MAIA's behavioural models identify the deviation from the analyst's personal baseline and the statistical improbability of the access pattern, triggering a quiet investigation before any data leaves the organisation.

Outcome: Exfiltration preparation identified — zero data compromised
Scenario 3 — Supply Chain Attack

Compromised Third-Party Integration

A trusted third-party data feed integration begins behaving subtly differently — not in a way that triggers any signature alert, but in a way that deviates from its established behavioural baseline: different query patterns, slightly altered timing, unexpected data volume changes. MAIA's adaptive baselines for this specific integration detect the deviation immediately, isolating the connection and preventing the compromised feed from being used as a lateral movement vector into the bank's core systems.

Outcome: Supply chain compromise contained at the network perimeter
Scenario 4 — Zero-Day Exploit

Novel Malware Without Any Known Signature

A piece of malware specifically engineered to evade all known detection signatures is introduced into the environment via a spear-phishing campaign against a treasury department employee. It begins establishing persistence and conducting reconnaissance. No signature exists; no rule can catch it. MAIA's behavioural models detect the new process's anomalous activity pattern immediately — unusual memory access patterns, unexpected outbound connections, atypical file system behaviour — and isolates the affected system within minutes.

Outcome: Novel malware contained in under 20 minutes with zero lateral spread
Scenario 5 — Regulatory Examination

DORA Audit — Six-Month Evidence Requirement

A regulatory examination requires a complete timeline of all material security events, investigations, and responses over the preceding six months — including evidence that continuous monitoring was in place, that incidents were detected promptly, and that containment actions were documented with explainable reasoning. MAIA's institutional memory surfaces the complete evidence package — every alert, every analyst action, every automated response — within hours, not weeks.

Outcome: Full six-month regulatory evidence package produced in under 4 hours
Scenario 6 — Infrastructure Change

Major Core Banking System Migration

The bank undertakes a six-month phased migration of its core banking system to a new platform. Throughout the migration, normal system behaviour changes radically and continuously. MAIA's adaptive baselines update in real-time as the migration proceeds — continuously distinguishing between the expected anomalies of a managed infrastructure change and any genuinely malicious activity attempting to exploit the disruption. No legitimate migration activity triggers false alarms; no threat hides in the migration noise.

Outcome: Zero false alarms during migration; two genuine threats detected and blocked

Explore MAIA's Complete Security Architecture

The Adaptive Intelligence Engine works in tandem with MAIA's Real-Time Threat Response capability — closing the loop from detection through containment at machine speed.

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