Organizations employ a defense in depth strategy to provide multiple layers of protection against cyber threats. However, traditional security tools often operate in silos, lacking integration to connect the dots between detection events across layers. Causal inference AI presents an opportunity to enhance defense in depth by modeling lateral connections between events across stack tiers. This whitepaper explains how causal analytics can strengthen alignment between defenses for more proactive protection.
In the face of rapidly evolving threats, relying on a single defensive layer like firewalls or antivirus is insufficient. Sophisticated attackers circumvent individual controls using techniques like zero-days, stolen credentials, and insider access. Defense in depth addresses this by deploying layered controls across stack tiers:
Network - Firewalls, IPS, proxies, microsegmentation
Endpoints - Antivirus, EDR, application control
Identity - MFA, SSO, IAM, user behavior analytics
Data - Encryption, DLP, database security
Applications - WAF, RASP, API gateways
Physical - Badges, cameras, locks
However, these disconnected controls often lack context sharing between layers. This gap can be filled with causal inference.
By modeling lateral relationships between detection events across stack tiers, causal inference can enhance defense in depth:
Network to Endpoints - Uncover hosts compromised based on lateral movement patterns
Endpoints to Identity - Identify credentials and accounts compromised based on endpoint malware
Identity to Data - Detect unauthorized data access connected to account misuse
Data to Applications - Link application attacks to breach of sensitive data
Applications to Physical - Tie unauthorized physical access to application credential abuse
Unlike correlations, causal models reveal the deeper attack narratives connecting events across layers. This enables earlier threat interception by understanding the lateral spread of multi-stage attacks across an environment.
Key benefits of applying causal inference to defense in depth:
Earlier threat detection by connecting precursor events across layers;
Improved visibility into the lateral propagation of threats;
Automated incident triage based on contextual severity inferred from cross-layer events;
Prioritized response using predicted impact analysis across infrastructure;
Optimized control integration based on causal dependencies learned between event layers;
Adaptive multi-layer security as models continuously learn new attack patterns;
With causal AI, defense in depth can evolve from a collection of disjointed controls into a coherent mesh of integrated protections.
However, challenges exist in implementing causal inference for defense in depth:
Requires large datasets encompassing diverse infrastructure layers;
Complex multi-stage attacks may have long lag times between events;
Careful feature engineering needed to capture contextual interactions between layers;
Difficult to validate accuracy of counterfactual predictions across environments;
Lack of model explainability impedes trust in causal inferences;
With investment in data pipelines, infrastructure visibility, and monitoring, organizations can systematically address these challenges and enhance security through applied causal AI.
Sophisticated cyber attacks necessitate a defense in depth approach spanning across stack tiers. But siloed security tools often lack the integration to connect events and uncover the lateral spread of threats across layers. By applying causal inference analytics, organizations can model multi-stage attack narratives linking detection events across network, endpoint, identity, data, application, and physical systems. This enables earlier threat interception, automated response, and optimized control alignment. With proper data foundations and oversight, causal AI can take defense in depth to the next level.
