Customer Snapshot
| Attribute | Details |
|---|---|
| Industry | Technology / SaaS (Application Development) |
| Cloud Environment | AWS (production and UAT) + private cloud |
| Workloads | Primarily VMs, Kubernetes being adopted for specific microservices |
| Developer Team | ~500–700 developers |
| AI Agents in Use | Google Gemini, Claude (POC phase, R&D team only) |
| Compliance | ISO 27001, SOC 2 |
| Existing Security Stack | OpenText Fortify (code scanning), SIEM (cloud events), MSSP-managed SOC, PAM/DAM (via partner) |
| Primary Interest | AI coding agent guardrails before org-wide rollout |
| Secondary Interest | Cloud misconfiguration and event monitoring (AWS CloudTrail, agentless) |
| Cloudanix Scope | Coding Agent Guardrail, Coding Agent JIT, CSPM, CDR |
The Situation: AI Agents Are Coming, and the Security Team Has No Visibility
This is a mid-to-large technology company whose core business is application development. Six hundred developers ship code every day across a mature AWS environment (production and UAT) supplemented by a private cloud. Their workloads are primarily VM-based, though Kubernetes adoption is underway for specific microservices as the organisation moves toward a microservices architecture.
The security team had done what most mature organisations do: assembled a capable stack over time. OpenText Fortify handles code scanning. All cloud events feed into a SIEM. The SOC is managed by an MSSP. Identity and access management is covered by PAM and DAM solutions implemented through a limited partner. All of these solutions were recently procured on multi-year contracts. The team had no appetite for consolidation, and ROI on existing investments was still being evaluated.
Then AI coding agents entered the picture.
The R&D team had started using Google Gemini and Claude in a POC capacity. The results were promising. Development velocity improved. The code quality was acceptable. Leadership saw the obvious trajectory: roll these tools out to the broader development organisation (500 to 700 developers).
But the security team saw something else: 600 developers, each with an AI coding agent that can read their entire local environment, has access to cloud credentials, and sends every prompt (including whatever secrets and PII it picks up) to an external LLM provider. With no guardrails implemented. No visibility into what the agents were doing. No policy enforcement. No audit trail.
The question was not whether to adopt AI coding agents. That decision was already made. The question was: how do you let 600 developers use Claude and Gemini safely, before a credential leak or a data exposure incident forces you to pull the plug entirely?
And while solving for AI agent security, the team had a secondary but equally concrete concern: their cloud monitoring stack fed everything into the SIEM, but they had no dedicated cloud misconfiguration posture management. Misconfigurations existed, CloudTrail events were being logged, but nobody was systematically identifying what was wrong, how findings connected, or what needed to be fixed first — especially as the infrastructure was evolving with Kubernetes adoption.
The Two Priorities
The conversation surfaced two clear workstreams:
-
AI coding agent security: Implement guardrails before the organisation-wide rollout covering DLP, credential protection, audit, and policy enforcement across all AI coding agents used by the development team.
-
Cloud misconfiguration and event monitoring: Gain agentless visibility into the AWS environment (misconfigurations, event-based anomalies, and compliance posture) without disrupting the existing security stack.
Both needed to work alongside (not replace) the current tooling. The existing Fortify, SIEM, PAM, and DAM investments were staying. Cloudanix needed to fill the gaps those tools were never designed to cover.
Where the Gaps Were
1. AI Coding Agents: 600 Developers, Zero Guardrails
The R&D team’s POC had proven the value of AI coding agents. But the POC also revealed the risk profile that nobody had tooling to address:
Credential exposure via prompts. When a developer asks Claude to “fix the deployment script” or “debug why the API call fails,” the agent reads the surrounding context including .env files, AWS credential files, API keys in configuration, and connection strings. That entire context is sent to the LLM provider’s API. There is no mechanism in Claude or Gemini to prevent this. The developer does not see it happening. The security team has no visibility.
PII in source and documentation. A 600-person development team working on a SaaS platform inevitably has customer data references, test fixtures with real data, internal notes with personal information, and configuration files with production values scattered across repositories. Every prompt that touches these files risks exfiltrating that data to an external provider.
No audit trail for agent activity. The security team could not answer basic questions: Which developers are using which agents? What are those agents accessing? Have any credentials been exposed? If a breach occurs, which agent session was responsible? For an organisation following ISO 27001 and SOC 2, this absence of auditability was a compliance gap in itself.
No policy enforcement. There was no mechanism to say “block prompts containing AWS keys,” “redact PII before it leaves the machine,” or “require approval before the agent executes a write operation on production.” The agents operated in a policy vacuum.
Uncontrolled cloud access by agents. Developers using AI coding agents were giving those agents access to cloud resources through long-lived credentials stored in their local environment. An agent with access to a developer’s ~/.aws/credentials file effectively has the same blast radius as that developer; indefinitely, without time-bounding, without audit, and without the ability to scope access to the specific task at hand.
Scale compounds everything. These risks exist for a single developer. At 600 developers, each running AI agents against production-adjacent environments, the probability of a credential leak or data exposure becomes a matter of when, not if. The security team needed guardrails in place before the rollout, not after the first incident.
2. Cloud Posture: Events Without Context
The organisation’s cloud monitoring model was built around a common pattern: feed CloudTrail events into a SIEM, let the MSSP-managed SOC triage alerts. This works for threat detection at the event level. It does not work for:
Posture management. Nobody was systematically scanning the AWS environment for misconfigurations overly permissive security groups, public S3 buckets, unencrypted volumes, IAM roles with excessive permissions. The SIEM detects events; it does not tell you that your infrastructure is misconfigured before an event occurs.
Configuration drift as workloads evolve. The organisation was adopting Kubernetes for specific products and moving toward microservices. Every new EKS cluster, every new service account, every new network policy introduced configuration surface area that was not being monitored. The gap between what was deployed and what was secure was growing with each sprint.
Compliance mapping. ISO 27001 and SOC 2 require demonstrable controls across cloud infrastructure. The SIEM provides event logs; it does not map infrastructure state against compliance frameworks or generate the evidence an auditor needs. The team was assembling this manually.
Event correlation. CloudTrail events in a SIEM are individual log lines. A role assumption from an unusual region, followed by an S3 access pattern change, followed by a new API key creation, these are three separate events in a SIEM. In a cloud detection and response system, they are one correlated anomaly chain that tells a story about potential compromise.
Agentless requirement. The team was explicit: whatever solution addressed cloud monitoring could not require agents deployed on their infrastructure. The existing VM and Kubernetes workloads were managed through established processes, and the team did not want additional agent overhead, especially on private cloud infrastructure where deployment models were more constrained.
The Cloudanix Solution
Coding Agent Guardrail: DLP Firewall for Every Developer’s AI Agent
The Cloudanix Coding Agent Guardrail addresses the AI agent security gap directly. It is an on-host DLP firewall that hooks into AI coding agents intercepting every prompt before it leaves the developer’s machine, with zero perceptible latency.
How it works for this team:
The guardrail installs as a lightweight binary on each developer’s machine. It hooks into the coding agent’s prompt flow via a universal hook interface supporting Claude, Kiro, OpenAI, Cursor, and Windsurf. When the developer interacts with the agent, the guardrail intercepts the outbound prompt before any network request is made (typically in under 1 millisecond) and scans it against the organisation’s policy.
What it catches:
- Secrets and credentials: AWS access keys, GCP service account keys, API tokens, SSH private keys, database connection strings, JWT signing secrets 2,000+ secret patterns matched via regex and entropy analysis.
- Personal data (PII): SSNs, credit card numbers, email addresses in fixtures, phone numbers, HIPAA-covered identifiers — classified and handled according to severity.
- Sensitive file reads:
.envfiles,~/.aws/credentials,*.pemprivate keys,kubeconfigfiles, Terraform state files, blocked from being included in prompts.
Policy-driven actions:
The security team defines policy as a YAML configuration checked into the repository. Each rule specifies a severity and an action:
- Block: The prompt is not forwarded to the LLM. The developer sees exactly what was caught and why.
- Redact: The sensitive value is replaced with a safe placeholder inline. The agent receives a useful prompt without the dangerous content.
- Warn: The developer is notified but the prompt proceeds. Used for lower-severity patterns where awareness matters more than prevention.
- Allow: Explicitly permitted patterns (e.g., test keys with known-safe prefixes).
For an organisation rolling out to 600 developers, this policy-as-code model means the security team defines rules once, version-controls them, and every developer’s agent inherits the same enforcement without per-developer configuration and without slowing down anyone’s workflow.
Fleet-wide visibility:
The guardrail’s fleet observability capability gives the security team a dashboard answering questions they currently cannot: How many developers have guardrails installed? Which agents are active across the organisation? What percentage of the fleet is on the latest policy version? Where are the enrollment gaps?
For a 600-developer rollout, this is not optional. It is how the security team confirms that the guardrail actually covers the organisation, rather than trusting that every developer installed it correctly.
Shadow AI discovery:
Before you can secure AI coding agents, you need to know which ones exist. The guardrail’s shadow AI discovery capability inventories every AI tool, IDE extension, MCP server, and runtime on each developer’s machine. For an organisation where the R&D team started with Gemini and Claude but developers may have individually adopted Cursor, Copilot, or others. This visibility is the baseline that any governance programme requires.
Centralised audit:
Every intercept (every block, every redaction, every warning) is logged with structured JSON containing the user, timestamp, matched rule, severity, and action. This stream integrates directly with the organisation’s existing SIEM, providing the security team and the MSSP with a new signal category: AI agent activity. For ISO 27001 and SOC 2, this audit trail demonstrates that controls exist over the AI coding agent surface; a requirement that will become explicit in future audit cycles.
Coding Agent JIT: Eliminating Long-Lived Credentials for AI Agents
The Coding Agent Guardrail prevents credential exfiltration. But the deeper question is: why do AI coding agents have long-lived credentials in the first place?
Coding Agent JIT eliminates the root cause. Instead of developers storing AWS keys in .env files for their agents to use, the agent requests access through Cloudanix’s JIT broker over MCP, and receives short-lived, scoped credentials that expire automatically when the task is complete.
How it works:
- The developer asks Claude to perform a task that requires cloud access (“fix the S3 bucket policy on prod-logs”).
- Claude invokes the Cloudanix JIT MCP server, specifying what resource it needs, what actions it requires, and for how long.
- Cloudanix evaluates the request against policy: Is this agent identity verified? Is the resource in scope? Is the action permitted at this sensitivity level?
- For write operations on production resources, a human approval gate triggers via Slack or Teams. The approver sees exactly what is being requested, by whom, and why.
- Cloudanix issues STS credentials scoped to the exact action, resource, and TTL. The agent never sees a long-lived key.
- The credentials auto-revoke the moment work ends or the time window expires. Every action during the session is logged with identity attribution “the human who asked, the agent that acted, the approver who permitted”.
Why this matters for 600 developers:
Without Coding Agent JIT, every developer who wants their agent to interact with AWS needs long-lived credentials stored locally. That is 600 sets of credentials, many of them with broader permissions than any single task requires, persisting indefinitely on developer machines that may be compromised, lost, or misconfigured. The blast radius is the full lifetime of every key; not the 47 seconds the agent actually needed.
With Coding Agent JIT, the credential surface collapses to zero standing privilege. No AWS keys in dotfiles. No shared service account credentials. No credentials that outlive their purpose. And a complete audit trail that maps every cloud action back to a specific human, a specific agent, and a specific task, precisely what ISO 27001 and SOC 2 auditors ask for.
CSPM: Agentless Cloud Posture Across AWS
The secondary requirement cloud misconfiguration and event monitoring, maps directly to Cloudanix’s core CNAPP capabilities. The deployment model is explicitly what the team requested: agentless, CloudTrail-based, and non-disruptive to existing tooling.
Agentless onboarding:
Cloudanix connects to the AWS environment via a read-only IAM role. No agents on VMs. No changes to running infrastructure. No impact on Kubernetes workloads. Within 30 minutes, the platform surfaces the complete posture baseline across the organisation’s AWS footprint.
What it covers:
- 1,000+ misconfiguration checks across IAM, networking, storage, compute, logging, encryption, and AWS-specific services.
- Security group assessment: Overly permissive ingress and egress rules, unused security groups, rules that expose management ports to the internet.
- IAM analysis: Over-permissioned roles, unused permissions, IAM policies with wildcard actions, cross-account trust relationships that should not exist.
- Storage posture: Public S3 buckets, unencrypted volumes, backup policies, lifecycle rules.
- Kubernetes security: As the organisation adopts EKS for microservices, KSPM extends automatically — covering pod security, IRSA misconfigurations, RBAC analysis, and network policy gaps.
Compliance mapping:
Every finding maps against the organisation’s ISO 27001 and SOC 2 frameworks. Rather than manually assembling evidence for audit cycles, the platform generates compliance posture continuously, showing which controls are met, which have gaps, and which findings need remediation to satisfy specific framework requirements.
For a team whose existing tooling does not provide this mapping, the immediate value is the reduction of audit-prep time from weeks of manual work to a dashboard that is always current.
GenAI-powered remediation:
Every finding includes actionable fix guidance. Not just a severity score and a description, but specific CLI commands, IAM policy changes, and security group modifications that the team can execute immediately. For an organisation where the security team relies on DevOps to remediate, the difference between “this security group is overly permissive” and “run this exact AWS CLI command to restrict ingress to your required CIDR range” determines whether findings get resolved or sit in a backlog.
CDR: Real-Time Event Monitoring on CloudTrail
The organisation was already feeding CloudTrail events into their SIEM. Cloudanix’s Cloud Detection and Response layer adds what the SIEM lacks: behavioural baselines, anomaly detection, and correlation.
What changes:
- Per-identity baselines. Cloud UEBA builds behavioural baselines for every identity, human users, service accounts, and now AI coding agents. When an identity deviates from its established pattern (unusual region, unusual API call, unusual time of day), the system detects it without a signature match.
- Event correlation. Individual CloudTrail events that look benign in isolation are correlated into anomaly chains. A role assumption from a new region, followed by an IAM key creation, followed by an S3 download pattern; this is one story, not three unrelated events.
- Adaptive notifications. Not every anomaly needs immediate attention. The platform’s adaptive notification system auto-snoozes known-benign patterns and escalates genuine deviations reducing the alert fatigue that plagues SIEM-only models.
- Integration with existing SOC. Cloudanix’s detection layer does not replace the MSSP-managed SOC. It enriches the signal. High-confidence anomalies can be forwarded to the SIEM and the SOC workflow, providing the MSSP with cloud-specific context that a generic SIEM rule set does not generate.
For a team where all security events already flow to a SIEM, Cloudanix adds the cloud-specific intelligence layer: posture-aware detection (the system knows a finding is more dangerous because the misconfiguration context says so), identity-aware correlation (the system knows who is acting and whether that is normal), and the connection between misconfiguration state and real-time events that a SIEM alone cannot provide.
The Additive Model: Filling Gaps Without Replacing Existing Investments
A critical aspect of this engagement: the organisation had no appetite for tool consolidation. Every existing solution (Fortify for code scanning, the SIEM, the MSSP, the PAM and DAM solutions) was recently procured on multi-year contracts. Cloudanix was explicitly positioned as additive, covering the two surfaces that no existing tool in their stack was designed to address:
| Existing Tool | What It Covers | The Gap Cloudanix Fills |
|---|---|---|
| OpenText Fortify | SAST / code vulnerabilities | Does not cover AI coding agent DLP, credential interception, or prompt-level policy |
| SIEM | Event log aggregation | Does not provide posture management, misconfiguration detection, or compliance mapping |
| MSSP-managed SOC | Alert triage and response | Does not have cloud-specific behavioural baselines or identity-level anomaly detection |
| PAM/DAM (partner) | Human privileged access and database monitoring | Does not cover AI agent credential governance or JIT for non-human identities |
The integration model is collaborative: Cloudanix’s audit stream feeds into the existing SIEM, providing the SOC with new signal categories (AI agent activity, cloud posture changes, identity anomalies). The MSSP continues to triage; they now have richer, cloud-specific context to work with.
This matters because the organisation’s security investment is not wasted. Every existing tool continues to do what it was purchased for. Cloudanix occupies the whitespace (the AI agent security layer and the cloud posture layer) that was invisible before these capabilities existed.
Preparing for the Rollout: A Phased Approach
The team’s timeline was clear: guardrails needed to be in place before the AI coding agent rollout to the full development team. The engagement follows a natural sequence:
Phase 1: Visibility (Week 1)
- Connect AWS environment (agentless, 30 minutes).
- Surface cloud posture baseline and compliance mapping.
- Deploy Coding Agent Guardrail to R&D team (where agents are already in use).
- Run shadow AI discovery across development machines.
- Establish audit stream to existing SIEM.
Phase 2: Policy & Enforcement (Weeks 2–3)
- Define organisation-wide guardrail policy (block/redact/warn rules for secrets, PII, sensitive files).
- Configure Coding Agent JIT for AWS access replacing long-lived credentials with short-lived, scoped grants.
- Set approval workflows for production-write operations via Slack.
- Validate fleet enrollment metrics and identify coverage gaps.
Phase 3: Organisation-Wide Rollout (Weeks 4+)
- Extend guardrail deployment to all 500–700 developers.
- Monitor fleet observability for enrollment completeness.
- Tune CDR baselines as agent activity data accumulates.
- Run first compliance evidence export (ISO 27001, SOC 2) against the new posture baseline.
This phased model means the security team has confidence in the guardrails before the broader development organisation gets access. No “deploy to everyone and hope nothing breaks.” Visibility first, policy second, scale third.
Platform Impact
30 min Agentless AWS onboarding | 600 devs Under guardrail coverage | Zero standing credentials Via Coding Agent JIT | 2 frameworks ISO 27001 + SOC 2 mapped continuously | Sub-ms latency On every prompt scan | 1 audit stream AI agent + cloud events into existing SIEM
Why Existing Tools Cannot Solve This
The organisation’s existing stack is mature and well-chosen for its original purpose. But none of it was designed for the two surfaces that have emerged in 2025–2026:
Fortify does not see AI agents. It scans code for vulnerabilities. It does not intercept prompts, does not enforce DLP on outbound LLM traffic, does not discover shadow AI tools on developer machines, and does not provide fleet-wide visibility into agent usage. Code scanning and AI agent security are related but distinct surfaces.
The SIEM does not manage posture. It ingests events and supports triage workflows. It does not scan infrastructure for misconfigurations, does not map findings against compliance frameworks, does not correlate events with posture context, and does not generate remediation guidance. Event monitoring and posture management are complementary, not interchangeable.
The PAM solution does not cover AI agent identities. It governs human privileged access; which is critical and should continue. But AI coding agents are a new identity category. They are not humans, not traditional service accounts, and not covered by PAM tools designed for interactive sessions. Coding Agent JIT is specifically built for this surface: credential brokering for agents via MCP, with human-in-the-loop approval and automatic revocation.
The MSSP does not have cloud-specific behavioural context. MSSPs excel at alert triage across enterprise tooling. But cloud-native anomaly detection (per-identity baselines, attack path correlation, AI-agent behavioural patterns) requires cloud-specific intelligence that generic SOC platforms do not provide. Cloudanix enriches the MSSP’s signal, not replaces their role.
The insight is not that existing tools are failing. It is that the attack surface has expanded into territory they were never designed to cover, and a purpose-built layer is needed for each new surface.
The Outcome
The organisation gains what it needed before the AI coding agent rollout: a guardrail layer that gives the security team confidence, visibility, and policy control over 600 developers using Claude and Gemini without slowing development velocity and without disrupting the coding workflow.
Simultaneously, the cloud environment that was previously monitored only through SIEM event logs now has continuous posture management, compliance mapping, and cloud-native detection and response. Which are agentless, non-disruptive, and integrated with the existing security operations workflow.
Key Results
✅ AI Agent Guardrails Before Rollout: DLP firewall deployed to development team with block/redact/warn policy for secrets, PII, and sensitive files. ✅ Zero Standing Credentials: Coding Agent JIT replaces long-lived AWS keys with short-lived, scoped grants via MCP for every AI agent interaction. ✅ Fleet-Wide Visibility: Shadow AI discovery and fleet observability across 600 developer machines, the security team knows which agents exist and which are covered. ✅ Agentless Cloud Posture: 1,000+ misconfiguration checks across AWS, with GenAI remediation guidance for every finding. ✅ Compliance Mapping: ISO 27001 and SOC 2 posture continuously mapped and evidence-exportable, reducing audit prep from weeks to hours. ✅ Cloud Detection & Response: Per-identity behavioural baselines, event correlation, and anomaly detection layered on top of existing SIEM/SOC workflow. ✅ Additive, Not Disruptive: Existing Fortify, SIEM, PAM, and DAM investments untouched. Cloudanix fills the gaps without consolidation pressure. ✅ 30-Minute Onboarding: Agentless, read-only AWS connection with immediate posture findings. ✅ SIEM Integration: Unified audit stream (AI agent activity + cloud events) feeding into existing SIEM for SOC triage.
Rolling Out AI Coding Agents to Hundreds of Developers Without Guardrails?
If your development team is adopting Claude, Gemini, Cursor, or Copilot and your security team has no visibility into what those agents are accessing, no DLP on outbound prompts, and no governance over the credentials those agents use, you are one prompt away from a credential leak at scale.
Cloudanix Coding Agent Guardrail deploys in minutes, intercepts every prompt before it reaches the LLM, and gives your security team the policy enforcement and audit trail they need — without adding latency to your developers’ workflow.
And if your AWS environment is monitored through SIEM but nobody is systematically managing cloud posture, Cloudanix connects agentlessly in 30 minutes and shows you what is misconfigured, how findings connect, and exactly what to fix first.
Book a Free Assessment see your AI agent risk surface and cloud posture through Cloudanix before your rollout begins.
Related Resources
- What is CNAPP (Cloud-Native Application Protection Platform)?
- What is CSPM (Cloud Security Posture Management)?
- Coding Agent Guardrail: DLP for AI Coding Agents
- Coding Agent JIT: Keyless Access for Claude, Kiro, Cursor, Codex
- AI Security: Securing the Infrastructure AI Agents Call
- Cloud Detection and Response (CDR)
- Cloud UEBA: Behavioural Analytics for Cloud Identities
- CSPM for ECS-Heavy AWS Workloads: Small Team, Multiple Accounts
- Securing Healthcare Applications: From Partner Code to Attack Path Closure
- Container Security and Real-Time Visibility for Modern Cloud
- Cloud Misconfiguration Checks for AWS
