AI‑Powered Threat Detection
CISA (Cybersecurity and Infrastructure Security Agency) was inundated with massive volumes of network and system telemetry, making manual threat analysis both time-consuming and error-prone. Proveo partnered with CISA to design and deploy a custom AI/ML platform that ingests real-time data, automatically flags high-risk anomalies, and ranks incidents by priority. Within weeks of deployment, CISA reported a 50 % faster incident response time and 12 % fewer false positives—dramatically improving overall security posture.
50%
faster incident response
12%
fewer false positives
The Cybersecurity and Infrastructure Security Agency (CISA) is the lead federal agency responsible for safeguarding the nation’s critical infrastructure from cyber threats. Tasked with monitoring government networks and coordinating responses to vulnerabilities, CISA’s security analysts work around the clock. However, as data volumes grew exponentially—encompassing logs from federal systems, third-party vendors, and on-premise networks—CISA needed a way to streamline detection without increasing headcount.
Diagnosing the Problem & Providing the Solution
Prior to Proveo’s engagement, CISA relied on manual workflows and legacy rule-based systems to sift through millions of log entries each day. Key pain points included:
- Data Overload: Analysts faced thousands of alerts daily, many of which were low-priority or false positives.
- Slow Response: Manual review and triage often introduced a 4-6 hour lag between detection and escalation.
- Accuracy Gaps: Existing signature-based tools missed novel or evolving threats, forcing analysts to spend precious hours on validation.
- Resource Constraints: With a fixed analyst team, scaling purely by hiring was neither feasible nor budget-justifiable.
CISA needed a solution that could ingest diverse data streams, learn normal behavior, and highlight truly suspicious activity—essentially acting as a force multiplier for their security operations.
Proveo created an AI-driven Threat Detection Platform with the following core components:
- Data Ingestion & Normalization
- Architecture: Built a scalable microservices pipeline using Python and Kafka to ingest network telemetry, firewall logs, endpoint data, and cloud provider logs.
- Normalization Layer: Applied a custom ETL process that unified disparate log schemas into a common data model. This ensured the AI/ML algorithms could analyze everything under a single “language.”
- Machine Learning Model Training
- Unsupervised Learning: Deployed autoencoder-based anomaly detection models (TensorFlow/PyTorch) that learned normal behavior patterns (baseline) across servers, applications, and user accounts.
- Feature Engineering: Generated features such as “average login time,” “data transfer volume,” and “unusual port access” to capture subtle deviations.
- Continuous Retraining: Designed a nightly pipeline that fine-tuned model weights based on the previous 24 hours of verified alerts, ensuring adaptation to evolving network patterns.
- Prioritization & Alerting
- Risk Scoring: Each detected anomaly was assigned a risk score (0–100) based on severity, target criticality (e.g., SCADA system vs. admin workstation), and historical context.
- Dashboard & Workflow: Delivered a React/Redux dashboard where analysts could view prioritized alerts, drill into raw logs, and annotate true/false positives—feeding those labels back to the model for future learning.
- Integration & Automation
- SOAR Integration: Connected the platform to CISA’s existing SOAR solution so that high-severity alerts automatically triggered incident playbooks (e.g., isolate host, block IP).
- API Layer: Exposed RESTful endpoints for CISA’s custom scripts to query threat intelligence feeds and automatically enrich alerts with external context (e.g., IP reputation, CVE associations).
The Results & Impact of Proveo
These figures showcase the measurable outcomes our solutions deliver—demonstrating efficiency gains, cost savings, and enhanced performance. Explore the stats below to see how Proveo’s tailored approach drives real business value.
50%
Faster Incident Response
Average triage time dropped from 4 hours to under 2 hours—allowing analysts to remediate critical alerts sooner.
12%
Fewer False Positives
By focusing on high-risk anomalies, the platform reduced analyst overhead, freeing up approximately 1,800 man-hours per year for advanced investigations.
30%
Increase in Novel Threat Detection
During the shadow-mode test phase, the AI models identified previously unseen threat vectors—proving its value against evolving adversaries.
$1.5M
Estimated Annual Savings
Factoring in reduced manpower for triage and fewer unnecessary incident playbook triggers, CISA’s security operations budget saw significant relief.
client feedback
What The Client Experienced
“Proveo’s AI platform transformed our SOC operations. We went from drowning in data to focusing on what truly matters. Their iterative approach, from proof of concept to full deployment in under three months, was nothing short of remarkable.”
CISA SOC Director
Key Takeaways & Next Steps
- Scalability: The microservices architecture easily scaled to accommodate new data feeds, including upcoming cloud-native telemetry.
- Adaptive Intelligence: The nightly retraining loop ensured the model stayed current with shifting baselines—critical as adversarial tactics evolved.
- Future Roadmap: CISA plans to extend the platform to support additional modules, such as AI-driven phishing detection and automated threat hunting scripts using LLM-powered playbooks.
As CISA embarks on the next phase, Proveo will continue to iterate—integrating threat intelligence feeds, expanding to OT/ICS environments, and exploring generative-AI for automated incident reporting.
