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| Feature | LLMs | Machine Learning |
|---|---|---|
| Input Format | Requires text (needs conversion or prompting) | Works directly with structured/tabular data |
| Performance on small datasets | Often worse unless fine-tuned on specific domain data | Generally better out of the box |
| Explainability | Low (black box) | Higher (especially with linear/logistic models or decision trees) |
| Training on small data | Needs heavy prompt engineering or fine-tuning | Well-suited for training on thousands or millions of rows |
| Inference speed | Slower (esp. large models) | Faster and cheaper |
| Flexibility | Can be used for many modalities (text/image/code/etc.) and unstructured data | Tuned to structured formats only |
| Benefit / Feature | Anomaly Detection | Rule-Based Systems |
|---|---|---|
| 1. Unknown Threat Detection | ✅ Detects zero-day attacks and novel behaviors via statistical deviations | ❌ Can only detect threats explicitly defined in rules |
| 2. Adaptability / Learning Over Time | ✅ Learns evolving patterns dynamically (e.g., changing login hours, new software) | ❌ Requires constant manual rule updates |
| 3. User Behavior Profiling | ✅ Builds baselines for individual users or groups | ❌ Applies generic rules to all users |
| 4. Peer Group Comparison | ✅ Detects users whose behavior deviates from others in their department or role | ❌ Cannot easily compare users against peers |
| 5. Time-of-Day or Geo Context Awareness | ✅ Flags unusual access based on time/location history | ⚠️ Possible with complex rules, but rarely implemented |
| 6. Lateral Movement Detection | ✅ Identifies cross-system or cross-group traversal that's abnormal | ⚠️ Requires preconfigured disallowed movement paths |
| 7. Privilege Escalation Detection | ✅ Flags sudden or gradual group membership changes that deviate from norm | ❌ Needs explicit rule per group or combination |
| 8. Device/Host Profiling | ✅ Detects new or compromised endpoints by learning communication patterns | ❌ Only detects listed suspicious hosts or known MAC/IPs |
| 9. Access Pattern Monitoring | ✅ Learns resource access patterns (e.g., file types, apps used) | ❌ Needs detailed manual rules for every pattern |
| 10. Beaconing / C2 Traffic Detection | ✅ Catches repetitive, low-volume, periodic traffic via time series modeling | ❌ Misses if destination is not on a blocklist |
| 11. Data Exfiltration Behavior | ✅ Identifies large or unusual uploads/downloads relative to user norm | ⚠️ Catches only if specific volume or domains are defined |
| 12. Insider Threat Detection | ✅ Flags subtle behavior changes over time like privilege creep or access abuse | ❌ Often missed if activity is within policy |
| 13. Multivariate Feature Correlation | ✅ Analyzes multiple dimensions (e.g., IP + file + time + frequency) to detect anomalies | ❌ Rules usually apply to one or two fields only |
| 14. Alert Prioritization / Risk Scoring | ✅ Can provide risk scores based on anomaly severity | ⚠️ Rules are binary — trigger or don’t trigger |
| 15. Scalability to Large Environments | ✅ Learns automatically across 1000s of users, devices, roles | ❌ Rules become unmanageable and prone to gaps at scale |
| 16. Seasonal / Cyclical Behavior Modeling | ✅ Detects expected spikes (e.g., end-of-month reporting) and flags deviations from seasonal baselines | ❌ No concept of seasonality or periodicity |
| 17. False Positive Reduction | ✅ Can reduce noise by learning what’s “normal noise” | ❌ Prone to frequent false positives if rules are too strict |
| 18. Deployment Flexibility | ⚠️ Needs training phase and model tuning | ✅ Easy to deploy with static rules |
| 19. Explainability / Audit Trail | ⚠️ Often needs supporting context to explain why something was flagged | ✅ Rules are explicit and easy to audit |
| 20. Compliance and Policy Enforcement | ⚠️ Not ideal for enforcing strict regulatory policies | ✅ Excellent for clear “must” and “must not” policy controls |
| Benefit / Use Case | Anomaly Detection | Rule-Based Systems |
|---|---|---|
| 1. Early Identification of Patient Deterioration | ✅ Detects subtle deviations in vitals, behavior, or lab results indicating risk before thresholds are crossed | ❌ Misses early signs unless values exceed predefined limits |
| 2. Detection of Emerging Risk Patterns | ✅ Learns complex patterns across labs, medications, claims, and lifestyle data | ❌ Can only detect what is explicitly encoded as a rule |
| 3. Predicting Hospital Readmissions | ✅ Uses historical and contextual data to model high-risk readmission profiles | ⚠️ Relies on static rules (e.g., “readmitted within 30 days”) |
| 4. Medication Adherence Monitoring | ✅ Flags unusual refill patterns, dosage changes, or usage gaps based on personal norms | ⚠️ Requires hard-coded schedules or refill intervals |
| 5. Identifying Fraud or Overutilization | ✅ Finds billing or usage patterns deviating from norm (e.g., duplicate procedures, excessive visits) | ❌ Needs specific rule for each type of abuse |
| 6. Tracking Chronic Disease Progression | ✅ Models patient trajectory over time (e.g., A1C drift, symptom severity) | ⚠️ Only flags when metrics exceed thresholds |
| 7. Stratifying Patient Risk Levels | ✅ Learns risk profiles across populations dynamically based on multivariate data | ⚠️ Often based on checklists or scorecard rules |
| 8. Outlier Detection in Clinical Practice | ✅ Identifies providers whose practices deviate significantly from peers | ⚠️ Requires manual definition of acceptable bounds |
| 9. Personalized Intervention Recommendations | ✅ Suggests interventions based on anomaly patterns in lifestyle, socioeconomic, or biometric data | ❌ Offers same interventions to similar rule hits |
| 10. Complex Comorbidity Interaction Handling | ✅ Learns nonlinear relationships among conditions (e.g., diabetes + COPD + depression) | ❌ Rules become exponentially harder to define with more variables |
| 11. Alert Fatigue Reduction | ✅ Reduces false alarms by learning what’s normal for each patient/population | ❌ Static rules often lead to excessive and low-value alerts |
| 12. Behavior-Driven Risk Detection | ✅ Detects lifestyle or psychosocial factors (e.g., activity drop, missed appointments) | ⚠️ Only captured if rules are built for each behavior pattern |
| 13. Tailoring Care Pathways | ✅ Adjusts care plans dynamically as patient patterns change | ❌ Predefined plans only adapt with manual review |
| 14. Resource Allocation Optimization | ✅ Forecasts future demand based on changing patient conditions and trends | ⚠️ Rules allocate based on averages, not predictive shifts |
| 15. Disease Outbreak Detection | ✅ Detects emerging clusters via syndromic surveillance, behavior changes, and health logs | ❌ Must wait for thresholds to be crossed or outbreak definitions triggered |
| 16. Population Surveillance at Scale | ✅ Scales across large health systems and adapts to regional differences | ❌ Becomes cumbersome as population diversity increases |
| 17. Social Determinants of Health (SDoH) | ✅ Correlates health outcomes with zip code, income, food access, housing instability, etc. | ⚠️ Needs manual coding of SDoH indicators and risk triggers |
| 18. COVID/Post-pandemic Health Pattern Shifts | ✅ Picks up deviations from pre-pandemic baselines across chronic care, mental health, etc. | ❌ Rules must be rebuilt to account for new norms |
| 19. Explainability for Clinicians | ⚠️ Requires supporting visualizations or risk score context | ✅ Simple to explain: “Rule triggered due to A1C > 9” |
| 20. Compliance and Protocol Adherence | ⚠️ Not ideal for strict protocol adherence monitoring | ✅ Excellent for ensuring procedures follow clinical guidelines |
