Cloud Security Posture Assessment & Remediation

Overview

Cloud environments become complex fast. Multiple accounts, multiple providers, and configuration drift outpace any single team's visibility. Our Cloud Security Posture Assessment identifies where that complexity has turned into real exposure, and turns it into a roadmap of concrete, prioritized actions your team can execute. If you'd rather have us execute them alongside you, implementation support is available as an extension of the same engagement.

What we assess

The assessment inventories the cloud services in use and examines security controls across:

  • Multi-cloud configuration and identity posture across AWS, Microsoft Azure, Google Cloud Platform, and Oracle Cloud Infrastructure
  • Containers, Kubernetes, and virtual machines
  • Networks, applications, and APIs
  • Data and identity protection, including privileged and excess-permission risk
  • Secure development practices and vulnerability management
  • Software supply chain risk: the open-source components and third-party dependencies your applications are built on
  • Compliance gaps against relevant frameworks and internal policy (e.g., SOC 2, ISO/IEC 27001, NIST CSF 2.0), see Cloud Security Governance & Playbook Development for how we help you align with them

Software supply chain risk gets particular attention because most modern software is not built from scratch. See Defense in Depth for Cloud Infrastructure for why that matters and how we approach it.

How we work

Assessments draw on native cloud security services (AWS GuardDuty, AWS Config, AWS Security Hub, Microsoft Defender for Cloud, Google Security Command Center) and, where it genuinely fits the client's environment, third-party platforms selected based on your infrastructure and constraints, not a partner relationship.

How we remediate

Findings turn into a remediation architecture built on three pillars:

  • Zero Trust architecture: aligned with NIST SP 800-207 and SP 800-207A for cloud-native applications across multicloud environments, with access controls based on user and workload identity, least-privilege access, increased traffic visibility, and encryption for connections and data in transit.
  • Application and API protection: virtual patching and Web Application and API Protection (WAAP) tailored to your application, reducing exposure to vulnerabilities exploitable through web and API traffic while permanent fixes are developed. Coverage is evaluated for each vulnerability, not assumed.
  • Continuous monitoring: Cloud Security Posture Management (CSPM), vulnerability scanning and patch management, Software Composition Analysis (SCA) and dependency monitoring, and SIEM to detect suspicious activity, including exploitation of vulnerabilities not yet remediated, plus containment mechanisms to limit the impact of an incident.

See Defense in Depth for Cloud Infrastructure for the full breakdown of how these pillars fit together.

Extending Cloud Posture to AI Workloads

As organizations adopt generative AI, the security challenge is rarely the model itself. It's the cloud infrastructure, network paths, and APIs around it. We apply the same cloud and network security discipline used for any critical workload to the infrastructure that runs and exposes AI services:

  • Secure cloud architecture: applying your cloud provider's well-architected best practices to the accounts, networks, and services that run AI workloads, and minimizing the attack surface exposed along the way.
  • Inspected connections: routing traffic to and from AI services through network and application security controls, so every connection is inspected rather than trusted by default.
  • Inference isolation: segmenting AI inference services from the rest of the environment, the same blast-radius reduction applied to any other critical workload.
  • AI-facing APIs: WAAP/WAF protection, service-to-service authentication, rate limiting, and input/output validation on the endpoints that expose a model to users or applications.
  • Continuous monitoring: extending posture monitoring and immutable logging to AI resources, so drift and anomalies surface the same way they would for any other cloud asset.
  • LLM Gateway adoption: consulting on deploying an LLM Gateway, including open-source options such as LiteLLM, as the central point for inspecting and governing traffic between your applications and the models they call.

For the agent-specific controls, prompt and response inspection, agent boundaries, that defend against prompt injection and MCP tool poisoning, see Generative AI Security Consulting.

What you get

A prioritized roadmap of concrete remediation actions, not a generic scan report, tailored to your existing tools, budget, risk profile, and operational maturity. Each finding comes with the specific steps needed to fix it, ranked by risk, so your team knows exactly what to do first. Findings are organized by the BRACE methodology's Baseline and Risk stages, so the roadmap connects directly to a governance plan your team can sustain (see Cloud Security Governance & Playbook Development).

Implementation Support (Optional)

The assessment and roadmap stand on their own. Some clients take the roadmap and run with it internally. For those who'd rather not carry the implementation load alone, we also offer hands-on support executing the plan: configuring the Zero Trust controls, deploying WAAP and virtual patching, and standing up the CSPM, SCA, and SIEM tooling the roadmap calls for.

Whether an engagement stops at the assessment and roadmap, or extends into implementation, is defined in the statement of work, not assumed. You choose the scope that fits your team's capacity.

See how this fits into the BRACE methodology