AI-powered security graphs help companies visualize network threats

AI-powered security graphs are transforming how organizations visualize and secure their digital environments, building on a decade-old concept with new intelligence capabilities. These enhanced visual maps help companies understand complex security relationships across their networks and create more effective, context-aware policies that move beyond the linear, rule-based approaches of traditional cybersecurity.

What you should know: Security graphs represent relationships between different resources in an organization’s environment as interconnected nodes and edges, creating visual maps that reveal previously invisible network dynamics.

• “What’s going on in our environments has been invisible to us for a long time, because we didn’t have the data,” says John Kindervag, chief evangelist at Illumio, a cybersecurity company, and creator of Zero Trust security architecture.
• Context and labels can be attached to each resource and relationship, providing detailed information about network components and their connections.
• These visual representations enable teams to fully grasp relationships between any two points in a transaction, making it easier to understand what security controls each requires.

How AI enhances security graphs: Machine learning models create more complete and accurate security visualizations by analyzing data from flow logs, resource inventories, and other environmental sources.

• AI can fill in missing details by making inferences based on knowledge from other deployments—for example, determining from flow logs that a given node is a web server.
• “It’s using AI to further annotate; to fill in the gaps and add additional context; or to refine, update, or correct context,” explains Raghu Nandakumara, vice president of Industry Strategy at Illumio.
• AI models help companies interpret security graphs to develop effective policies by identifying unexpected patterns and prioritizing which events and relationships deserve investigation.

Why this matters: Traditional linear security policies often create contradictory rules or ineffective controls because they lack contextual understanding of network transactions.

• Legacy approaches match traffic against sequential rules without understanding the broader context of each transaction.
• “Does this meet rule 1? Nope. Does it meet rule 2? Nope. But it meets rule 46, so we’re going to enforce that rule, except that may be a bad thing, because it doesn’t have any context at all other than sequence,” Kindervag explains.
• AI-powered security graphs enable policy alignment with actual transaction flows, creating more intelligent and effective security controls.

Real-world applications: AI models can identify traffic patterns that may be normal at certain times but suspicious at others, enabling dynamic policy development.

• Tools like the Illumio Platform use security graphs to help companies create effective policies and automatically detect potential threats.
• “Essentially, they help find the unexpected and prioritize which events and relationships are worth investigating more than others,” Nandakumara says.
• This approach enables security teams to focus on the most critical risks while automated systems handle routine threat detection and elimination.