For years, security leaders have focused on one major question: Where is our sensitive data?
Although addressing that question is necessary, finding data is only the beginning. Modern enterprise data constantly moves among applications, microservices, cloud workloads, APIs, AI models, and third-party systems.
Today, the core security question has evolved. We no longer just ask where sensitive data lives, but what it is, why it is there, and what happens if one of those systems is compromised.
To answer these questions, organizations must bridge the gap between data intelligence and infrastructure enforcement. The integration between Cyera and Akamai Guardicore Segmentation delivers the solution by turning data classification into operational, data-aware network control.
Traditionally, network security and segmentation policies relied on technical attributes: IP addresses, subnets, operating systems, or cloud tags. A database that processed public marketing assets often received the same default network controls as a server that was handling payment card information (PCI) or protected health information (PHI) if they shared an operating system or subnet.
By integrating Cyera’s deep data discovery and classification with Akamai Guardicore Segmentation, organizations can transform static data context into active security controls.
[ Cyera DSPM ]
• Discovers Sensitive Data
• Classifies PII / PHI / IP
• Evaluates Data Risk
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│ Imports Metadata (Labels)
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[ Akamai Guardicore Segmentation ]
• Exposure Analysis & Reachability
• Real-time Traffic Visibility
• Data-Aware Microsegmentation PolicyThe integration operationalizes governance via:
Automated metadata importing
Context-aware labeling
Data-aware segmentation policy rules
Cyera automatically discovers and classifies sensitive data across multicloud and on-premises environments. It maps data sensitivity, data categories (e.g., PII, PCI, IP), and compliance mandates (e.g., the General Data Protection Regulation [GDPR], the Health Insurance Portability and Accountability Act [HIPAA], and Title 23 of the New York Codes, Rules, and Regulations, Part 500 [23 NYCRR 500]).
These insights flow directly into Akamai Guardicore Segmentation as asset labels. A workload is no longer just Production-Web-Server-17; it is dynamically labeled as an asset containing Restricted-Financial-Data or AI-Training-Pipeline.
Security teams can create precise policy rules based on what the asset contains. For instance, Akamai Guardicore Segmentation can automatically block lateral communications between unverified workloads and any server bearing a Cyera label for sensitive customer records.
A critical vulnerability on a workload that contains public data carries a fundamentally different risk profile than the exact same vulnerability on a workload that contains customer identities.
However, evaluating data sensitivity alone still leaves a gap. To accurately prioritize risk, security teams must unite data context with exposure context:
Data sensitivity (Cyera): Identifies what the data is and how valuable or regulated it is
Exposure analysis (Akamai’s Exposure Analysis and Response (ExAR) capabilities): Determines whether an asset is reachable from compromised systems, maps active network dependencies, and measures blast radius
Segmentation (Akamai Guardicore Segmentation): Enforces microsegmentation boundaries to isolate assets and eliminate unauthorized lateral movement
Instead of treating vulnerabilities, network reachability, and data sensitivity as disconnected problems, this unified approach evaluates them as parts of a single risk equation. A vulnerability becomes an immediate priority when exposure analysis shows a direct network path, and Cyera confirms the target contains highly regulated enterprise data.
The rapid adoption of enterprise AI and large language models (LLM) pipelines makes this dynamic context even more critical. AI models do not operate in isolation; they continuously pull data across vector databases, software as a service (SaaS) integrations, training repositories, and autonomous agents.
Frameworks like the National Institute of Standards and Technology (NIST) AI Risk Management Framework and regulations such as the EU AI Act emphasize strict data governance across training and inference workflows. When AI models consume data that was never originally intended for AI processing, the risk of accidental exposure or data leaks increases dramatically.
As AI capabilities accelerate the speed at which attackers perform reconnaissance and map attack paths, security teams must first understand these relationships. By pairing Cyera's visibility into AI data stores with Akamai Guardicore Segmentation’s control over workload communications, organizations can allow AI teams to innovate rapidly while ensuring sensitive datasets remain segmented from unauthorized access.
Bridging data intelligence and microsegmentation enables a mature, continuous risk model across five distinct stages as shown in the table.
| Stage | Security action | Platform role |
|---|---|---|
Discover | Identify all workloads, applications, and data repositories | Cyera and Akamai Guardicore Segmentation |
| Classify | Determine sensitivity, regulatory mandates, and data types | Cyera’s data security posture management |
| Contextualize | Map active communications, attack paths, and exposure | Akamai’s ExAR capabilities |
| Control | Enforce microsegmentation policies based on data labels | Akamai Guardicore Segmentation |
| Assure | Continuously validate policy efficacy as workloads scale | Integrated ecosystem |
The five stages of bridging data intelligence and microsegmentation
Security and compliance should act as guardrails, not speed limits. The easiest ways to eliminate risk are to reject block cloud migrations, restrict AI projects, and stall software deployments. But slowing down the business creates a different kind of enterprise risk.
When security controls are informed by granular data context, enforcement becomes precise rather than broad. Cloud and application teams can modernize at speed, governance, risk, and compliance (GRC) teams gain real-time visibility instead of relying on manual spreadsheets, and CISOs can protect high-value assets without introducing friction across the enterprise.
Knowing where sensitive data lives only answers one question. Knowing where data can travel and what can communicate with it — and automatically enforcing those boundaries — is how modern enterprises can move forward safely.
Standard network rules rely on technical attributes like IP addresses and subnets, which fail to track dynamic data flows across modern AI and LLM pipelines. Combining Cyera’s visibility into vector databases with Akamai Guardicore Segmentation isolates training repositories and prevents unauthorized workloads from reaching sensitive AI datasets.
Pairing Cyera's data classification with Akamai's ExAR capabilities allows security teams to evaluate true risk by linking data value directly to network reachability. A software vulnerability becomes an immediate remediation priority only when ExAR reveals a clear attack path to a workload that Cyera confirms contains regulated enterprise data.
The integration automates governance for major mandates, including GDPR, HIPAA, and 23 NYCRR 500. It also enforces the strict data access boundaries required for AI workflows under the NIST AI Risk Management Framework and the EU AI Act.
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