Model Performance & Security

MLSecOps teams monitoring production AI models

35 Models Healthy

7 At Risk

Last Scan: 21:03:16

42

Production Models

35 healthy, 7 at risk

8

Drift Detected

5 data drift, 3 concept drift

127

Security Events (24h)

89 adversarial, 38 extraction

1,847,293

API Calls (24h)

Avg latency: 42.3 ms

Optimal

Model Performance Metrics

Model Name0	Version0	Status	Accuracy0	Acc. Trend	Data Drift0	Pred. Drift0	Latency (ms)0	Req/Hour0
fraud-detection-xgb	2.3.1	Healthy	94.3%	-1.2%	0.02	0.02	23	45,230
customer-churn-nn	1.8.0	Warning	87.6%	-3.5%	0.09	0.07	45	12,450
recommendation-engine	3.1.0	Healthy	91.2%	0.8%	0.01	0.01	38	89,320
sentiment-analysis-bert	2.0.0	Critical	79.8%	-5.2%	0.15	0.13	92	23,100
image-classifier-resnet	4.2.1	Healthy	96.7%	0.3%	0.01	0.01	15	67,800

Model Accuracy Degradation Over Time

fraud-detection

customer-churn

recommendation

Avg Degradation

-2.3%/month

Models Below Threshold

4

Retrain Required

3 Data Drift Detection Alerts

customer-churn-nn

Feature 'account_age' distribution shifted significantly

Data Drift | KS Statistic: 0.087

20:48

sentiment-analysis-bert

Prediction accuracy dropped below threshold

Concept Drift | KS Statistic: 0.152

20:33

fraud-detection-xgb

Minor shift in transaction amount distribution

Data Drift | KS Statistic: 0.023

20:03

Adversarial Attack Detection

Detections

Time	Model	Attack Type	Confidence	Action Taken
20:58:16	image-classifier-resnet	FGSM	92%	Input rejected
20:51:16	fraud-detection-xgb	PGD	87%	Alert triggered
20:38:16	sentiment-analysis-bert	TextFooler	78%	Logged for review

API Usage Anomalies

/api/v1/predict

Unusual spike in requests 4532 calls from IP: 192.168.1.105

/api/v1/batch

Large batch size detected 85 calls from User: api_user_429

/api/v1/extract

Potential extraction attempt 1203 calls from IP: 10.0.0.42

Resource Consumption & Performance Impact

CPU Usage

65%

GPU Usage

78%

Memory Usage

72% (115.2 GB / 160 GB)

Security monitoring overhead: 8.5% of total resources

Model Versioning & Rollback Statistics

Model	Current Ver	Deployments	Rollbacks	Success Rate
fraud-detection	v@version.CurrentVersion	23	2	91%
customer-churn	v@version.CurrentVersion	15	3	80%
recommendation	v@version.CurrentVersion	31	1	97%

Avg Rollback Time

3.2 min

Auto-Rollback Enabled

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