Move From Cloud Security Alerts to Decisions You Can Explain
Digital Intelligence is designed for the gap between “AWS produced a finding” and “the security team knows what happened, what it affects, what matters most, and what should happen next.” DI connects resource state, exposure, identity, vulnerability, data, runtime, drift, behavior, and evidence into one AWS-focused operating view.
Prioritize Risk Using More Than Severity
A critical CVE, public route, over-privileged role, sensitive-data signal, or runtime finding can each be important. The real priority emerges when those facts are connected.
Is the resource actually reachable?
DI can connect route, security-group, endpoint, load-balancer, and validated reachability evidence instead of assuming exposure from labels alone.
Who can reach or change it?
Bring IAM trust, Access Analyzer evidence, observed behavior, and role relationships into the same risk decision.
What is the impact if it fails?
Use application boundaries, criticality, data sensitivity, environment, ownership, and incident context to focus remediation effort.
Build Attack Paths From Evidence, Not Proximity
Two AWS resources being near each other in an inventory does not prove that one can reach, assume, modify, or exploit the other. DI's architecture is designed to use actual network, IAM, route, endpoint, policy, and trust evidence when building paths.
Investigate With Bounded, Traceable Evidence
Query the smallest relevant window
Use CloudWatch Logs, CloudTrail, S3/Athena, Security Lake, and other authorized sources only when the investigation needs deeper evidence.
Keep IDs, hashes, timestamps, and source references
DI investigations are designed to preserve where evidence came from and distinguish source evidence from analyst or AI interpretation.
Say what is verified and what is not
Inspector conclusions can distinguish VERIFIED, STRONG EVIDENCE, INFERENCE, UNKNOWN, and REQUIRES LIVE VALIDATION instead of presenting every conclusion with the same certainty.
Keep Raw AWS Telemetry Where It Already Belongs
DI's default architecture is federated and query-in-place. That reduces duplicated storage and avoids making WordPress/MySQL another raw cloud log warehouse.
Compact security intelligence
Resource identity, normalized configuration, relationships, snapshots, drift, findings, incidents, evidence pointers, hashes, and case-specific extracts.
High-volume raw telemetry
CloudWatch logs, long-term CloudTrail data, VPC Flow Logs, WAF logs, EKS audit logs, large database logs, and other high-volume sources remain customer-owned by default.
See Coverage Gaps Instead of False Reassurance
Security teams need to know when a service is disabled, a Region is unsupported, an IAM permission is missing, a collector is stale, or a scan is incomplete.
Track Change Over Time, Not Just Today's Snapshot
What changed?
Track created, removed, and security-relevant configuration changes across the normalized cloud estate.
Who changed it?
Where evidence exists, correlate CloudTrail actor context, account, Region, time, and affected resource.
Why does the change matter?
Connect drift to public exposure, encryption, logging, IAM trust, backup protection, firewall state, routes, data risk, and incidents.
Use AWS-Native Security Without Losing Cross-Signal Context
DI can preserve the native source finding while adding Aegisify context around it. This avoids turning every provider signal into an opaque proprietary result and reduces the need to rebuild AWS-native security engines inside the Aegisify product.
Make Security Capacity Visible
The Protected Workload model gives customers a visible way to understand DI capacity across modern AWS estates instead of hiding utilization behind user seats or account counts.
Same meter in product and contract
Dashboard utilization and billing capacity use the same versioned Protected Workload formula.
Not limited to EC2
The approved formula includes active serverless, application containers, managed runtimes, and normalized image/identity coverage.
Usage can exceed 100%
Customers can see when they are approaching or exceeding licensed capacity instead of discovering a true-up without prior visibility.
Product Discipline Is Part of the Security Model
Raw telemetry is not copied by default
DI keeps compact intelligence and case evidence locally while high-volume sources remain customer-controlled in AWS.
AI can explain evidence, not fabricate it
Deterministic signals and source evidence come first. Inspector can help connect evidence and recommend action, but AI output is not treated as proof.
Technical evidence is not certification
A passed AWS control or DI report does not by itself prove a federal control is met or a system is authorized.
AWS depth before provider breadth
DI IaaS is AWS-focused today. Future provider support should be built with provider-specific evidence rather than pretending every cloud works the same way.
What Changes for the Security Team?
Does DI reduce the number of AWS findings?
DI's goal is not to hide findings. The goal is to normalize and correlate them so teams can prioritize what matters, understand evidence, and identify which conditions combine into material risk.
Can DI investigate without ingesting all logs?
Yes. The intended architecture queries the smallest relevant time window and source set, then stores only the evidence needed for the case and its audit trail.
Does DI automatically remediate everything?
No. Recommendations are advisory by default. Consequential remediation should use separate write permissions, deterministic preconditions, explicit approval, rollback, evidence preservation, and post-change validation.
How can Aegisify AI help?
Ask about Aegisify or WordPress: errors, plugins, security, SEO, compatibility, troubleshooting, comparisons, or launch a free website scan.