Threat detection & classification
Attack patterns & prioritization evolve constantly - payload mutations, obfuscation variants, automated abuse, policy changes, public disclosures, etc. Traditional ML-based detection systems can’t keep pace because updating them requires retraining, validation, and redeployment cycles that take days to weeks. In the meantime, teams rely on manual rules and brittle patches. Whether you are building a threat detection product or supplementing 3rd party tools, this fundamental challenge is a constant.
Orca removes this lag entirely.
Instant updates, no retraining
Orca’s memory-controlled model architecture lets you update detection behavior by simply editing the model’s attached memoryset. Adding new malicious samples or adjusting labels immediately changes the model’s output - no training run, no pipeline, no redeploy.
Observed result: A customer reduced threat-response time from ~3 weeks → 3 minutes
Why traditional defenses lag
High drift
Attackers mutate payloads quickly; static models degrade
Rules-enging stopgaps for lagging AI updates
AI native incorporation of new threats needed to prevent easy evasion adaptation from hackers
Slow MLOps
Retraining + QA + deployment cycles introduce multi-week gaps
Low visibility
Teams can't see why a model allowed / blocked a request
How Orca improves API threat detection
1. Real-time threat incorporation
Update the memoryset with new attack examples → model behavior updates instantly. No regression risk, no retraining budget
2. Per-endpoint or per-customer customization
Swap memorysets on each inference to reflect different APIs, geos, customer environments, or partner risk profiles - without maintaining separate models
3. Full explainability for security workflows
Each detection links to the exact memory items referenced during inference. Engineers can inspect root cause, correct misclassifications, and audit decisions cleanly
4. Robustness to drift
Orca’s retrieval-augmented classifiers maintain accuracy where conventional models degrade, even under aggressive distribution shifts. This occurs in all models but is exacerbated in security where the adversarial dynamic forces additional drift
Example workflow
1. New malicious payload variant is observed
2. Analyst adds 1 - 5 examples to the memoryset
3. Model output changes immediately - no reploy.
4. Orca Inspector shows which memory items influenced future detections.
5. Teams validate and iterate in minutes, not weeks.
Who this fits
Security teams needing:
- Rapid adaptation to new attack vectors
- Inline, low-latency threat scoring
- Clear audit trails for detection logic
- Customizable logic per customer, endpoint, or region
- Reduced operational load from retraining pipelines
Talk to Orca
Speak to our engineering team to learn how we can help you unlock high performance agentic AI / LLM evaluation, real-time adaptive ML, and accelerated AI operations.