Alert Prioritisation: Decision-Theoretic Methods

• Alert Prioritisation (AP) is a framework of methodologies that rank alerts by balancing timely action benefits against the cost of interruptions.
• It leverages decision theory, cost optimization, reinforcement learning, and human feedback to dynamically prioritize critical events.
• Applications span cybersecurity, cloud operations, and disaster response, demonstrating improved efficiency and reduced false negatives.

Alert prioritisation (AP) is a set of methodologies, models, and systems for mediating, sorting, or ranking alerts in a way that optimally directs attention and resources toward critical events under operational, economic, or cognitive constraints. AP is foundational across domains that contend with high alert volumes, including human–computer interaction, industrial cloud infrastructures, security operations, participatory sensing, disaster response, and business processes. Central to recent AP research are cost-sensitive decision-theoretic models, machine learning–based classifiers, reinforcement learning, collaborative and human-in-the-loop schemes, and domain-specific optimization principles.

1. Principles and Mathematical Foundations

Much of the foundational AP literature establishes the problem as an optimization under uncertainty and competing costs. In "Attention-Sensitive Alerting," utility-directed procedures formalize this as computing the net expected value of issuing an alert, balancing the benefit of timely delivery ($\text{EVTA}$) and the cost of interruption ($\text{ECA}$) (1301.6707). The decision is made under the rule:

$$\text{NEVA} = \text{EVTA} - \text{ECA}, \text{ and an alert is sent if } \text{NEVA} > 0$$

Where:

• $\text{ECA} = \sum_j C_a(A_i, F_j) \cdot p(F_j|E^a)$, with $C_a$ as the cost of alert $A_i$ in attentional state $F_j$, and $p(F_j|E^a)$ as the probability (from a Bayesian model) of attentional state $F_j$ given evidence $E^a$.
• The cost of deferred alerts is separately modeled as the expected cost of delayed action (ECDA) or for email as expected cost of delayed review (ECDR), accounting for time-criticality.

In process and risk management, AP utilizes cost models and Monte Carlo simulations (MCS) to numerically estimate the impact of risks (1803.08706, 2405.20679). Quantitative methods allow for objective, numeric prioritization rather than subjective matrix ranking.

2. Machine Learning and Automated Classification

Machine learning has become a dominant mechanism for AP, especially in settings where scale or data complexity preclude manual triage. In cybersecurity operations, the Automated Alert Classification and Triage (AACT) system (2505.09843) uses supervised gradient boosting and learns both static and dynamic features from SOC analyst history. Dynamic features are counts of analyst actions for alert categories or entities over short and long lookbacks. The system computes, for instance,

$$\alpha_{a_i}^c = \sum_{\{t', s\,\in\,[t-\delta, t)\}} \mathbb{I}(A^c(t', s) = a_i)$$

and normalizes these for classification, enabling rapid scoring and queue reduction while minimizing false negatives.

In the context of managed SOCs, frameworks like TEQ (2302.06648) train separate models for alert "content" and "context," combine their scores (e.g., $y_{ensemble} = 0.7 y_{content} + 0.3 y_{context}$), and organize alert queues so that actionable incidents are consistently surfaced. These systems also rely on operational metrics—response time, false positive suppression rates, within-incident prioritization—linked directly to deployment realities.

3. Decision-Theoretic and Cost-Optimized Methods

A unifying trend is the inclusion of explicit cost models in which AP operates not just to sort alerts, but to prescribe interventions and optimize resource usage. "Alarm-Based Prescriptive Process Monitoring" (1803.08706) formulates the decision to raise an alert using:

• a parameterized cost tuple: $(c_{in}, c_{out}, c_{com}, eff)$
• expected outcomes to weigh the cost of (i) intervention, (ii) undesired outcome, (iii) compensation for unnecessary intervention, and (iv) mitigation effectiveness,
• empirical threshold tuning to minimize total incurred process cost.

Such frameworks often implement threshold-based or optimization-driven rules, ensuring that the AP mechanism is sensitive to economic or operational return on investment (ROI), such as the requirement $eff \cdot c_{out} > c_{in}$ for beneficial alarm triggering.

4. Human-in-the-Loop and Human–AI Teaming

The limitations of AI-only AP, especially with rare, novel, or context-dependent events, are increasingly addressed via Human–AI Teaming (HAT) strategies. "Adaptive alert prioritisation in security operations centres via learning to defer with human feedback" (2506.18462) introduces an L2DHF (Learning to Defer with Human Feedback) framework, where a deep reinforcement learning (DRL) agent dynamically decides whether to trust the AI’s prioritization or defer to a human analyst. The system updates its deferral policy using real-time feedback, maximizing:

$$\mathbb{E}\left[\sum_{k=0}^\infty \gamma^k r(s, a)\right]$$

where $r(s, a)$ rewards correct non-deferral for easy cases and deferral for ambiguous ones, with extra rewards for minimizing misprioritization of high-severity alerts.

Such adaptive schemes have demonstrated substantial gains in both prioritization accuracy and reduction in analyst workload, particularly for critical alert categories.

5. Robustness, Adversarial Settings, and Real-Time Adaptation

A recurring challenge in AP is robustness to strategic adversaries or evolving environments. In "Finding Needles in a Moving Haystack," the prioritization problem is cast as a defender–attacker game where policies are learned using adversarial reinforcement learning within a double-oracle framework (1906.08805). Policies are dynamic, stochastic, and optimized as best responses in a mixed-strategy game, enabling defenders to allocate investigative resources under adversarial uncertainty.

Recent work leverages soft actor-critic (SAC) reinforcement learning (e.g., SAC-AP (2207.13666)) to improve exploration and policy robustness in stochastic settings, resulting in significant reductions in loss compared to baseline methods.

AP algorithms, especially in SOCs, are further challenged by the need for real-time updateability and adaptation. Frameworks such as AACT (2505.09843) and TEQ (2302.06648) integrate automated feedback loops and time-series cross-validation, directly addressing performance decay and evolving alert schemas.

6. Domain-Specific and Collaborative Approaches

Across application domains, AP is tailored via domain-specific models and collaborative mechanisms:

• In cloud operations, anti-patterns of alerts (e.g., unclear names, misleading severity, cascading/repeating alerts) are empirically identified and mitigated through aggregation, correlation analysis, and future proposals for automatic quality evaluation (2204.09670).
• Disaster response AP systems combine GIS-derived spatial layers with Bayesian networks to efficiently synthesize heterogeneous, large-scale evidence into intelligible, action-focused rankings, employing full-probability distributions weighted for risk (2506.18423).
• Participatory sensing platforms implement collaborative, content-aware assessment among peer nodes, using threshold-based voting or querying to prevent low-quality or duplicate alerts from congesting networks (2312.09957).

In all these systems, care is given to optimizing for both technical and ethical dimensions—especially privacy, transparency, bias mitigation, and explainability.

7. Empirical Evaluation and Practical Impact

Across the surveyed literature, AP solutions are evaluated using both domain-specific operational metrics and general machine learning criteria. Examples include:

• For SOC AP, metrics such as analyst queue reduction (up to 61%), false negative rate (as low as 1.36%), and response time improvements (over 22%) are reported (2505.09843, 2302.06648).
• In prescriptive monitoring, ROI conditions and overall process costs are benchmarked before and after AP deployment (1803.08706).
• Frameworks like RAPID use recall, precision, F1 at multiple granularities, and throughput (logs/second) to validate improved detection and prioritization (2406.05362).
• Risk prioritization in project management demonstrates that Monte Carlo–based methods provide differentiated, objective rankings, often diverging from traditional risk matrices (2405.20679).
• In participatory and infrastructure-less systems, network delivery ratios, message counts, and latency are used to show practical message reduction while preserving information quality (2312.09957).

A salient feature of advanced AP systems is the shift from heuristic or static priority assignment to dynamically optimized, context-sensitive, and learning-driven triage integrated into real-world workflows.

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Alert prioritisation continues to develop as a cross-disciplinary subfield, integrating decision theory, machine learning, human–AI interaction, and systems engineering. Across all these facets, effective AP is distinguished by responsiveness to uncertainty, efficient use of resources (attention, time, computational budget), adaptation to dynamic environments, and empirical grounding in operational outcomes.