AI-Centric Cybersecurity
Evaluating next-generation, AI-centric security stacks for a Fortune 100 firm to deliver adaptive threat detection without swamping analysts or violating explainability mandates.
Client
Fortune 100 Financial Services Firm
Objective
Cut MTTD by ≥50% with <3% False Positives
Timeline
10-Week Sprint
Key Focus
Explainability & Compliance
The Challenge: Three Barriers to Enterprise Rollout
AI-driven security analytics promise self-learning detection of zero-day threats, but three persistent barriers slow their adoption in the enterprise.
High False-Positive Rates
Overly sensitive models can drown security analysts in thousands of alerts, hiding real incidents in a sea of noise and leading to alert fatigue.
Adversarial AI & Data Poisoning
Attackers can now craft malicious data to "poison" the training set, teaching the model to ignore their attack footprints.
Explainability & Compliance
Regulators and auditors demand clear "how/why" evidence for automated security decisions, a requirement that black-box AI models often fail to meet.
Key Outcomes: A Resilient & Explainable Security Stack
Our 6-phase sprint, which included adversarial stress tests, identified five lead platforms. Pilot results on live traffic showed a dramatic improvement over legacy systems.
56%
Lower Mean-Time-To-Detect (MTTD)
2.6 / 100k
False Positives per 100,000 Events
Five Lead Platforms Identified:
- Graph Neural-Net Anomaly Engine: 96% zero-day recall with built-in Shapley attribution for every alert.
- Transformer-based Log Language Model: Detects novel TTP sequences with counterfactual explanations.
- Adversarial-Hardened Autoencoder: Resists data poisoning with differential-privacy noise and ensemble voting.
- Explainable Risk Score API: Generates natural-language justifications tied to MITRE ATT&CK tactics for auditors.
- SOAR-native Playbook Integrator: Auto-pushes triage actions, reducing analyst dwell time by 52%.
Strategic Impact
The firm selected the graph-neural-net engine plus the explainable risk-score API for a 5,000-endpoint pilot. The successful rollout will cut incident-response time in half, satisfy regulator demands for "explainability," and position the company as a leader in resilient, AI-centric cybersecurity.