Vulnerability Management and Pentesting
Find Every Weakness Before Attackers Do
Periodic scanning and annual penetration tests are no longer sufficient. Modern threat actors move faster than quarterly assessment cycles. We deliver continuous vulnerability intelligence combined with rigorous human-led testing that covers your entire attack surface—including the AI systems most tools don't know how to assess.
The Gap Between What You Know and What's Exploitable
The average enterprise has thousands of vulnerabilities at any given time. The critical question isn't how many—it's which ones are actually reachable by an attacker, actually exploitable in your specific environment, and actively being weaponized in the wild. Treating every high-severity CVE with equal urgency is a fast path to alert fatigue and missed critical exposures.
Modern vulnerability management combines three prioritization signals: CVSS severity scores for theoretical impact, EPSS probability scores for exploitation likelihood, and the CISA Known Exploited Vulnerabilities (KEV) catalog for confirmed in-the-wild abuse. Together, these signals let security teams focus remediation effort where actual risk lives—not where theoretical severity points.
For organizations deploying AI systems, the challenge compounds. AI models, training pipelines, inference APIs, and agentic workflows introduce attack surfaces that traditional vulnerability scanners were never designed to assess. A comprehensive program covers both the infrastructure layer and the AI-specific layer—because attackers don't restrict themselves to one or the other.
Our Assessment Methodology
Asset Discovery & Attack Surface Mapping
Enumerate your complete attack surface—infrastructure, cloud workloads, AI endpoints, third-party integrations, and shadow IT. You cannot protect what you cannot see, and unknown assets are attackers' preferred entry points.
Vulnerability Scanning & SBOM Analysis
Continuous authenticated scanning across your environment, including software bill of materials (SBOM) analysis for known vulnerabilities in dependencies and supply chain components embedded in AI model containers and inference services.
Risk-Based Prioritization
Layer CVSS severity, EPSS exploitation probability, and CISA KEV confirmation against your asset criticality and network exposure. Generate a prioritized remediation queue that directs engineering effort toward vulnerabilities attackers are actually using—not just theoretical worst cases.
Penetration Testing
Human-led adversarial simulation that chains vulnerabilities into realistic attack scenarios—identifying paths that automated scanners miss because they require creativity, context, and multi-step reasoning. Testing scoped to your threat model and business-critical systems.
AI-Specific Attack Surface Testing
Specialized assessment of AI systems for vulnerabilities unique to machine learning: adversarial input manipulation, training data poisoning vectors, model extraction via API abuse, prompt injection in LLM-powered features, and agent privilege escalation paths.
Remediation Validation & Reporting
Verify that fixes actually close the vulnerabilities they target—not just satisfy a ticket. Deliver executive-ready risk summaries alongside technical findings with exploitation evidence, business impact context, and sequenced remediation guidance.
Assessment Types We Deliver
Tailored engagements for different environments, risk profiles, and compliance requirements.
Infrastructure Penetration Testing
Internal and external network assessments simulating attacker behavior against your perimeter, internal segments, and critical systems. Covers network services, Active Directory, cloud environments, and lateral movement paths.
Web Application & API Testing
Manual and automated assessment of web applications and APIs against OWASP Top 10 and API Security Top 10. Critical for AI-powered applications where APIs expose model inference, data retrieval, and agentic functionality.
AI/ML System Security Assessment
Dedicated testing for machine learning systems: model robustness against adversarial examples, data pipeline integrity, inference API security, embedding extraction risk, and agentic workflow privilege boundaries.
Cloud Security Configuration Review
Identify misconfigured cloud resources, overpermissive IAM policies, and exposed storage where AI training data, model artifacts, or inference infrastructure reside. Aligned to CIS Benchmarks and cloud provider security baselines.
Supply Chain & Dependency Analysis
Assess third-party libraries, open-source models, pre-trained weights, and vendor integrations for known vulnerabilities and integrity risks. SBOM-driven analysis ensures visibility into components across your AI delivery chain.
Continuous Vulnerability Management
Ongoing vulnerability intelligence feeding a risk-prioritized remediation program—not a point-in-time snapshot. Includes integration with your ticketing and patch management systems, SLA tracking, and monthly risk posture reporting.
Framework Alignment
Our programs align with established standards for vulnerability management, penetration testing, and AI-specific security assessment.
NIST SP 800-40r4 — Patch Management
Six-stage enterprise patch management lifecycle aligned to NIST guidance. Prioritization integrates CVSS severity, EPSS exploitation likelihood, and CISA KEV confirmed-exploitation status against asset criticality tiers.
PTES — Penetration Testing Execution Standard
Structured engagement methodology covering pre-engagement, intelligence gathering, threat modeling, exploitation, post-exploitation, and reporting. Ensures consistent, repeatable assessment quality across engagements.
OWASP Top 10 for LLM Applications
Purpose-built testing coverage for the most critical AI/LLM vulnerabilities: prompt injection, insecure output handling, training data poisoning, model denial of service, and supply chain risk specific to AI-powered systems.
MITRE ATT&CK & ATLAS
Penetration testing scenarios mapped to documented real-world attacker techniques. MITRE ATLAS extends coverage to AI-specific attack patterns, enabling testing against tactics used by actual threat actors against ML systems.
The Sentinel Nexus Approach
We lead every engagement with threat modeling before any scanning or testing begins. Understanding your specific adversary profile, business-critical assets, and regulatory obligations determines where testing effort delivers maximum value—and prevents the common failure mode of comprehensive coverage that somehow misses the assets that actually matter.
Our assessments are delivered by practitioners, not platforms. Automated tools are essential for coverage at scale, but chaining vulnerabilities into realistic attack paths, identifying logic flaws in AI workflows, and surfacing business impact beyond CVSS scores requires human judgment that no scanner provides. Our findings include exploitation evidence—not just theoretical risk scores.
Findings don't live in a PDF. We integrate with your ticketing, patch management, and SIEM systems to ensure vulnerabilities flow directly into remediation workflows with appropriate SLAs based on risk tier. Follow-up validation confirms that fixes hold before the finding is closed.
AI Red Teaming for Deeper Adversarial Testing
Penetration testing identifies vulnerabilities. AI red teaming goes further—simulating coordinated adversarial campaigns against LLMs, agents, and ML pipelines using multi-agent attack simulations.
Learn about AI Red Teaming →Governance & Compliance Documentation
Penetration test reports and vulnerability management data support regulatory compliance requirements under NIST CSF, SOC 2, ISO 27001, and EU AI Act audit obligations.
Learn about AI Governance →Ready to find your exposures before attackers do?
Let's scope an assessment program that matches your environment, threat model, and remediation capacity.
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