Enterprise Security for Technology Companies

ExposureManagementwithAIAgentsforTechnologyCompanies

Shift from volume-based vulnerability backlogs to exposure decisions grounded in how attackers operate across code, cloud, and production.

0AI-generated code contains security vulnerabilities. Code generation tools accelerate development but introduce exploitable flaws at scale.
0Technology-sector breaches were financially motivated. Attack activity is driven by profit, making exploitability a stronger signal than theoretical severity.
0Average cost of a data breach in the global technology industry. Breach impact continues to rise as cloud sprawl and attacker speed increase.
0Technology firms take over 100 days to remediate critical vulnerabilities. Slow patching cycles leave exploitable exposure open across production environments.
The Challenge

WhyPrioritizationBreaksintheTechnologySector

Modern tech stacks are built for speed and scale, not clean security queues. Exposure can originate from code paths, identity permissions, cloud posture, or internet-facing services. Prioritization fails when these signals are handled in siloes, because issues that appear to be low risk when assessed individually, can be a critical vulnerability, when mapped together to create a full attack path. Teams end up fixing what looks urgent, while exploitable exposure remains open.

  • App, cloud, and identity signals evaluated in isolation masking what is truly exploitable
  • Vulnerability volume makes it challenging to surface what is critical
  • Attack-path context is missing across services and assets
  • Alert fatigue delays remediation and failure to meet SLAs

Walk through your exposure with an expert →

EAP

ExposureAssessmentPlatform

Gain a single exposure view across applications, cloud infrastructure, APIs, and production environments. Decisions reflect real exploitability and business impact, not disconnected findings.

  • Attack Surface Discovery
    Continuously surface external and internal assets as code ships, infrastructure scales, and environments change.
  • Vulnerability Prioritization
    Order vulnerabilities based on exploitability, asset criticality, and environment context, not raw severity scores.
  • Finding Correlation
    Deduplicate and connect findings across scanners and security tools to cut noise and expose real attack paths.
  • Reporting
    Generate structured, audit-ready reports with clear prioritization, supporting evidence, and remediation status for engineering and leadership review.
Exposure Assessment
Live
142
Web Apps
89
APIs
216
Cloud
1,847
Endpoints
Prioritized Findings
Critical
12
High
47
Medium
183
Low
412
2,294
Assets
654
Findings
3%
Actionable
AEV

AdversarialExposureValidation

Move beyond theoretical risk. Validate exploitability in real conditions before attackers do, so remediation effort targets what truly matters.

  • Exploitation Testing
    Safely verify which vulnerabilities are usable in live technology environments.
  • Attack Path Analysis
    Understand how attackers chain misconfigurations, identities, and vulnerabilities to reach critical services.
  • Exploit Validation in CI/CD
    Validate exploitability in pipelines before it reaches production
  • Evidence-Based Reporting
    Capture proof of exploitability with clear evidence to support remediation and security decisions.
Adversarial Validation
In Progress
Recon
Exploit
Validate
Report
Attack Paths Identified
TargetChainExploitableRisk
Payment API3 hopsYESCritical
Auth Service2 hopsYESHigh
Admin Portal4 hopsNOMedium
Data Store5 hopsYESCritical
4
Paths Found
3/4
Exploitable
75%
Validated
AI-Powered

Strobes AI Agents Advantages

Purpose-built AI agents for modern technology environments, not generic LLM wrappers, operating across code, cloud, APIs, and production.

Autonomous Triage

AI agents continuously analyze and prioritize findings without manual intervention—development teams can't manually review every vulnerability in AI-generated code and rapid releases

Context-Aware Reasoning

Evaluates exposure using application criticality, deployment context, and production impact across code repositories, cloud infrastructure, and container environments

Workflow Automation

Coordinates remediation across security, engineering, and DevOps teams with context preserved through CI/CD pipelines

Natural Language Queries

Ask questions about your exposure posture in plain language — "Which vulnerabilities affect our production API?" "What's exploitable in our main branch?"

24/7 Operations

Never-sleeping agents monitor exposure changes in real-time across code commits, infrastructure changes, and production deployments

Privacy & Guardrails

Data remains in your environment. AI operates under policy-enforced boundaries with human approval for production changes and complete activity logs

Stop Audit Scrambles with AI Agents Capturing Evidence in Real Time

AI agents capture exposure validation and remediation context as work happens. Compliance reporting becomes structured output from exposure management, not a separate audit exercise. Decisions remain traceable, defensible, and aligned to business and regulatory objectives.

SOC 2 · ISO 27001 · GDPR · NIST · PCI DSS

Compliance

Built-in Regulatory Alignment

Defensible Evidence, Captured in Flow

Exposure validation and remediation activities are recorded with context as they occur, eliminating reconstruction during audits.

  • Exploitability-backed records: Evidence reflects validated exposure rather than raw severity
  • Remediation linkage: Actions and status updates remain tied to the originating exposure
  • Business context association: Findings connect to critical systems and operational impact
  • Structured reporting foundation: Evidence is organized for consistent reporting output

Security decisions are supported by documented proof, not retrospective explanation.

Key Insight

WhenExternalValidationExposedGapsAcrossAppsandAWS

Internal testing covered every release, and AWS configurations followed best practices. Still, external exposure surfaced in ways that didn't align until applications, APIs, and AWS controls were examined together through an independent lens.

Read the Case Study →

100+
Integrations

Aggregate findings from every scanner and security tool in your stack

70%
Noise Reduced

Deduplication and risk-based prioritization cut through alert fatigue

24h
Mean Time to Fix

Critical findings remediated within SLA with automated routing

3%
Actionable Risk

Focus on the findings that actually matter to your business

CTEM Framework

From Signal to Action — Continuous Exposure Management

In technology environments, exposure breaks down when each stage operates in isolation. Strobes aligns the full exposure lifecycle so prioritization, validation, and response reflect real production risk.

01

Scoping

Define critical services, sensitive data flows, and high-value assets. Security effort stays focused on what would cause real business or customer impact.

02

Discovery

Continuously surface assets, APIs, cloud resources, and exposures across code, infrastructure, and production as environments evolve.

03

Prioritization

Elevate exposures that attackers can realistically exploit, factoring in production impact, data sensitivity, and blast radius. Severity alone never dictates action.

04

Validation

Confirm exploitability in real conditions before committing remediation effort. Assumptions are removed and teams act on proven exposure.

05

Mobilization

Remediation moves forward with evidence and context aligned to engineering workflows, CI/CD pipelines, and deployment cycles.

FAQ

Frequently Asked Questions

Your Exposure Won't Wait. Neither Should Your Response.

Take control of your exposure with Strobes and reduce remediation time by up to 67%