AI security posture management for LLM platforms

Bring enterprise LLM risk posture into a unified view

Traditional security posture management was built around infrastructure, applications, and data, but AI introduces another layer of context that security teams need to understand. An AI system can draw from sensitive data, act through delegated permissions, connect to external tools, depend on third-party models or services, and change how it behaves as its inputs, integrations, and operating context change.

AI security posture management (AI-SPM) is the continuous practice of discovering the AI systems an organization uses, assessing its configuration, access, and data exposure against security controls, and fixing the gaps before it leads to an incident. It applies to AI models, agents, third-party AI services, and the large language model (LLM) platforms teams rely on.

As an AI-SPM tool, SecureAI focuses on the the LLM platforms your teams use. It assesses enterprise accounts from Anthropic Claude, Google Gemini, and GitHub Copilot, scores each one, and tracks how its posture changes over time.

Where LLM account posture breaks down

Most LLM posture problems are not targeted attacks. They are ordinary security hygiene issues that become harder to manage because LLM platforms are adopted quickly, by many teams, under several different vendor accounts.

API key sprawl

API keys are a common way applications and scripts reach an LLM, and they accumulate fast. Keys get generic names that say nothing about their purpose, they stay active long after the project that needed them has ended, and they go unrotated for months. Keys owned by service accounts are especially easy to lose track of because no individual feels responsible for them.

Identity and access that outgrows its purpose

LLM accounts often begin with a small group of administrators and then expand as more people are invited. Without regular attention, the account ends up with more members and broader roles than its current use requires, which makes it harder to know who can change settings, create keys, or view usage data.

Gaps in monitoring, network, and data controls

Logging, network restrictions, and data handling settings are frequently left in their initial state. When monitoring is incomplete, a security team cannot reconstruct what happened in the account after a problem is reported, and when network access is unrestricted, a leaked key can be used from anywhere.

Third-party integrations and code security

LLM platforms connect to other tools, and each integration extends trust to another vendor. For coding assistants such as GitHub Copilot, the risk extends further into the software delivery process, which is why code security and CI/CD controls belong in the posture picture for those accounts.

AI security posture management for the platforms your teams use

Each LLM platform exposes its own administrative controls, so SecureAI assesses every account against the control domains that apply to that platform, covering areas such as API key security, identity and access management, monitoring and logging, network access control, and third-party integrations, with CI/CD security and code security added for GitHub Copilot. Every account is scored on the same scale and tracked in the same view, which lets a security team compare posture across vendors and see which accounts need attention first. With SecureAI, you can currently assess the enterprise accounts on the following LLM platforms:

• Anthropic Claude
• Google Gemini
• GitHub Copilot

How AI security posture management works

SecureAI's LLM risk assessment connects to the enterprise accounts an organization uses and evaluates each one against a set of enterprise security controls.

1. Assess each account by control domain: The assessment examines identity, API key, data, network, monitoring, third-party integrations, and code security controls.
2. Work from indicator-level findings: Within each control domain, SecureAI lists the individual risk indicators it checked, along with a severity rating and a risk exposure percentage.
3. Remediate failing controls: The assessment guides remediation for the issues it finds, and each failing indicator names the specific control that needs attention.
4. Score each account and track it over time: Each assessed account receives an AI risk score, calculated from weighted control violations and banded as low, medium, or high risk. A score trend chart shows how an account's posture changes over time, which helps a team show that remediation work is having an effect.
5. Satisfy audit requirements: Each platform's assessment can be exported and shared with compliance and audit teams.

Building an AI security posture management program

A mature AI-SPM program follows a repeatable sequence, and each step depends on the one before it.

1. Account for every LLM platform in use: Identify the vendor accounts that teams rely on, including separate instances run by different departments, and connect each one to the assessment.
2. Assess each account against security controls: Evaluate every account by control domain so that findings point to specific configuration areas instead of general concerns.
3. Prioritize by severity and exposure: Start with the indicators rated most severe and those with the widest exposure, since a failing control that affects most keys or members matters more than one that affects a single item.
4. Fix the gaps and assign responsibility: Route each finding to the team that administers the account, and resolve issues such as unrotated keys, unused keys, and unattributed spend at the source.
5. Reassess on a schedule: Set a regular sync time so the assessment reflects current configuration, and watch the score trend to confirm that fixes are holding.
6. Share the evidence: Export assessments for compliance and audit teams, and use the score trend to demonstrate how AI risk is changing over time.