AI Security Posture Management for CISOs
See the AI systems, agents and models running across your enterprise — and the risk each one carries.
Indrasol helps security leaders discover AI across the business, assess AI security and governance exposure, prioritize risk in business terms, and stand up an AI security posture management program you can defend to the board, to auditors and to enterprise customers.
- Discover AI applications, models, agents, data flows, APIs and integrations — including shadow AI
- Assess AI security, governance and compliance risk across the full AI lifecycle
- Prioritize by business exposure, so remediation effort maps to what the board actually asks about
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AI Security & Governance Expertise
Security, governance and compliance handled as one program, not three.
Enterprise Cybersecurity Experience
A security practice delivering for enterprise environments since 2010.
Practical Assessment & Remediation
Findings come with a remediation path and hands-on support, not just a report.
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Related practices:
The visibility gap
Can You Answer the Board's Three Questions About AI?
In many enterprises, AI adoption now moves faster than the security program that has to account for it. Business units ship copilots, agents and model integrations through SaaS platforms, cloud services and developer tooling — frequently without a security review, and frequently without appearing in any asset inventory. That leaves the CISO answering for systems the security function cannot yet see.
Where is AI running?
Which applications, models, agents and third-party AI features are live across business units — and who owns each one.
What can it reach?
What data, systems, identities and privileges each AI system touches, directly and through its integrations.
What are we doing about it?
Which AI risks are accepted, which are being remediated, and how posture is trending quarter over quarter.
Where the visibility gap usually sits
- Shadow AI — Unsanctioned tools and embedded AI features adopted outside the security review path.
- Agent identity and access — Non-human identities holding standing privileges with no clear owner.
- Prompt injection and output handling — Untrusted input reaching systems that act on model output.
- Sensitive data movement — Regulated or confidential data flowing into prompts, embeddings and vector stores.
- Model and supply chain risk — Third-party models, fine-tunes, datasets and open-source AI components.
- Evidence gaps — No defensible way to demonstrate AI governance to auditors, customers or regulators.
Not sure where AI is running across your business units?
Definition
What Is AI Security Posture Management (AI-SPM)?
AI Security Posture Management (AI-SPM) is the continuous practice of discovering the AI systems an organization runs, mapping the data, identities and infrastructure each one touches, assessing its security and governance risk, and prioritizing remediation — so AI risk is measured and managed the way cloud and application risk already are.
What AI-SPM discovers
- AI applications and copilots in business use
- Foundation models, fine-tuned models and hosted endpoints
- AI agents and agentic workflows, including tool and function access
- Prompt, retrieval and RAG pipelines
- Vector databases and embedding stores
- Training, fine-tuning and evaluation datasets
- AI APIs, SDKs, plugins and MCP-style connectors
- Non-human identities, keys and service accounts used by AI systems
- Third-party and embedded AI inside existing SaaS platforms
What AI-SPM evaluates
- Data exposure — what sensitive data enters, persists in and leaves each system
- Identity and access — privileges held by AI agents and the humans behind them
- Model security — prompt injection, jailbreak, extraction and evasion exposure
- Supply chain integrity — provenance of models, datasets and AI dependencies
- Infrastructure posture — cloud, container and network configuration behind AI workloads
- Guardrails — input filtering, output handling, rate limits, human-in-the-loop
- Observability — whether AI activity is visible to the SOC
- Governance alignment — NIST AI RMF, ISO/IEC 42001, OWASP LLM Top 10, MITRE ATLAS, EU AI Act
- Business impact — what a failure in each system would cost in data, customers or operations
The case for AI-SPM
Why CISOs Need AI Security Posture Management Now
AI does not simply add one more asset class to the attack surface. It adds a category of asset that reasons over sensitive data, holds credentials, calls other systems and takes action — while sitting outside many existing inventories. Established controls still apply, but few tools in the standard stack were designed to answer the question a board will ask: what is our AI risk, and is it going down?
Shadow AI adoption · Prompt injection and jailbreaks · Sensitive data leakage into prompts · Training and fine-tuning data exposure · Over-privileged AI agents · Non-human identity sprawl · Insecure model supply chain · Vector store and RAG exposure · Model theft and extraction · Unsafe or unvalidated model output · Third-party and embedded AI risk · Missing AI logging and detection · Unprovable AI governance and compliance
The path from AI adoption to managed posture
- 1. AI adoption
Business units ship AI faster than review cycles allow.
- 2. AI visibility
AI systems, agents and integrations are discovered and given owners.
- 3. Risk context
Each finding is tied to data, identity and business impact.
- 4. Continuous posture
Posture is measured, tracked and reported on a cadence.
- 5. Secure scale
New AI initiatives launch inside guardrails, not around them.
Lifecycle
The AI-SPM Lifecycle: Six Steps a CISO Can Operationalize
A posture program only earns its place if it produces something the security function can run every quarter. These six steps are the operating loop.
01 — Discover
Build a working inventory of AI systems, agents, models, data flows and integrations, including shadow AI across business units.
Outcome: A single, owned AI asset register.
02 — Understand
Map each system's data, identities, privileges, dependencies and business purpose.
Outcome: Context you can reason about, not a raw asset list.
03 — Assess
Test security, governance and compliance exposure against recognized AI frameworks and your own control set.
Outcome: Findings with evidence behind them.
04 — Prioritize
Rank findings by exposure and business impact rather than raw severity score.
Outcome: A defensible remediation sequence.
05 — Protect & Implement
Apply guardrails, access boundaries, monitoring and secure-by-design patterns alongside your teams.
Outcome: Controls in place, not findings alone.
06 — Monitor & Improve
Re-baseline as the AI estate changes and new systems come online.
Outcome: Posture you can trend and report.
Scope of work
What an Indrasol AI-SPM Engagement Covers
AI Discovery & Inventory
Identify AI applications, models, agents, pipelines and embedded AI across cloud, SaaS and developer environments — then assign ownership to each.
AI Security Risk Assessment
Assess exposure across prompts, models, data, identities and infrastructure, with findings evidenced and mapped to your environment.
AI Governance & Compliance Alignment
Align AI practice with recognized frameworks and customer expectations, and bridge to ISO/IEC 42001, SOC 2, ISO 27001 and CMMC work already in flight.
AI Agent & Agentic AI Security
Review agent permissions, tool access, delegation chains, memory and human-in-the-loop boundaries before autonomy scales.
AI Model Security
Examine prompt injection, jailbreak, extraction, poisoning and unsafe-output handling across hosted, open-source and fine-tuned models.
AI Supply Chain Security
Review provenance and integrity of third-party models, datasets, plugins, connectors and open-source AI dependencies.
AI Risk Prioritization & Board Reporting
Translate technical findings into business exposure, with metrics and narrative a CISO can take into a board or audit committee session.
Continuous Posture Improvement
Establish cadence, ownership and re-baselining so posture keeps pace with AI adoption.
Engagement model
How an AI-SPM Engagement Progresses
01 — Scope & Discovery Kickoff
Agree scope, business units, environments and success criteria with your security and AI stakeholders.
Outcome: A scoped plan and named owners.
02 — AI Asset & Context Mapping
Discover and map AI systems, agents, data flows, identities and integrations.
Outcome: An AI inventory with ownership and context.
03 — Risk Assessment & Prioritization
Assess security, governance and compliance exposure, then rank by business impact.
Outcome: A prioritized AI risk register.
04 — Control Recommendations
Define guardrails, access boundaries, monitoring and governance controls suited to your stack.
Outcome: A practical control roadmap.
05 — Remediation Support
Work alongside your security, platform and AI engineering teams to implement the roadmap.
Outcome: Controls implemented and re-tested.
06 — Monitor & Improve
Establish reporting cadence, metrics and re-baselining as the AI estate grows.
Outcome: AI posture you can report each quarter.
Comparison
AI-SPM vs CSPM, CNAPP, DLP and Traditional GRC
Security leaders reasonably ask whether existing investments already cover this. Most cover part of it. The table below shows where each approach stops.
| Approach | Primary focus | What it sees | AI context | Continuous AI posture |
|---|---|---|---|---|
| AI-SPM | AI systems, agents, models and their risk | AI inventory, data flows, identities, guardrails | Native | Yes |
| CSPM | Cloud configuration and control-plane risk | Cloud resources and misconfigurations | Limited | Infrastructure only |
| CNAPP | Cloud workload and application protection | Workloads, containers, cloud applications | Limited | Partial |
| Vulnerability Management | Known software vulnerabilities | CVEs in hosts, images and packages | Minimal | No |
| IAM | Human and service identity governance | Accounts, roles and entitlements | Partial — agents often unmanaged | No |
| DLP | Data movement and loss prevention | Files, endpoints and channels | Partial — prompts often unseen | No |
| AI Governance (policy) | Policy, principles and accountability | Documented process and intent | Native | Point-in-time |
| AI Runtime Security | Blocking attacks at inference time | Live prompts and responses | Native | Runtime only |
| Traditional GRC | Control evidence and audit readiness | Policies, controls and artifacts | Limited | Periodic |
These approaches are complementary, not competing. AI-SPM is the layer that ties them together for AI: it supplies the inventory and risk context the others assume you already have.
Outcomes
Turn AI Visibility Gaps Into a Measurable Risk Picture
01
A working AI inventory
AI systems, agents and integrations discovered, owned and documented.
02
Risk expressed in business terms
Findings tied to data, customers, revenue and regulatory exposure.
03
A prioritized remediation path
Sequenced work your security, platform and AI teams can actually execute.
04
Posture you can report
Metrics and trend lines for the board, auditors and enterprise customers.
Who this is for
AI-SPM for the Industries Under the Most Scrutiny
- SaaS and B2B software — AI features in the product bring enterprise security questionnaires with them.
- AI-native startups — Enterprise buyers often ask for evidence of AI security before a large contract.
- FinTech — AI decisioning meets model risk, SOC 2 and financial regulator expectations.
- HealthTech — AI touching PHI raises immediate privacy and patient-safety obligations.
- Cloud and managed service providers — AI deployed on behalf of clients inherits their risk.
- Defense and government contractors — AI use must align with CMMC and federal expectations.
- Manufacturing and enterprise technology — AI in operations extends risk into physical processes.
Why Indrasol
Why CISOs Work With Indrasol on AI Security Posture
- A security-first approach to AI adoption, not a policy-only exercise
- Practical AI security posture assessment with evidence behind each finding
- Security, governance and compliance aligned inside one program
- Risk prioritized by exposure and business context, not by severity score alone
- Support across assessment, implementation, monitoring and improvement
- Enterprise delivery since 2010 — 100+ experts, 50+ enterprise clients, 15+ industries
Frequently asked questions
AI Security Posture Management: CISO FAQ
?What is AI Security Posture Management (AI-SPM)?
AI-SPM is the continuous practice of discovering the AI systems an organization runs, mapping the data, identities and infrastructure each one touches, assessing security and governance risk, and prioritizing remediation. It gives security leaders a measurable view of AI risk rather than a point-in-time policy review.
?Why do CISOs need AI-SPM?
Because accountability for AI risk sits with the CISO even when AI adoption does not run through security. AI-SPM closes that gap: it produces an owned inventory of AI systems, ties each one to the data and privileges it can reach, and turns AI risk into something that can be reported and trended like any other risk domain.
?How is AI-SPM different from CSPM?
CSPM secures cloud configuration — resources, control planes and misconfigurations. AI-SPM secures the AI layer running on top of that cloud: models, agents, prompts, training data, vector stores and the non-human identities AI systems use. CSPM will not tell you what an AI copilot can reach. The two are complementary.
?What is the difference between AI security and AI-SPM?
AI security is the broad discipline of protecting AI systems. AI-SPM is the operating model that makes it manageable: continuous discovery, risk context, prioritization and measurement. AI security says what good looks like; AI-SPM tells you where you currently stand and what to fix next.
?What does an AI security risk assessment include?
A typical assessment covers AI discovery and inventory, data exposure, identity and access for both humans and agents, model security including prompt injection and extraction, supply chain integrity for models and datasets, guardrail coverage, logging and detection, and governance alignment — with each finding mapped to business impact.
?What is AI agent security, and why does it matter for agentic AI?
AI agents act: they hold credentials, call tools and APIs, and chain decisions with limited human review. Agent security covers permissions, tool and function access, delegation chains, memory handling and human-in-the-loop boundaries. As autonomy increases, an over-privileged agent becomes a short path from a prompt to a production system.
?How does AI-SPM support AI governance frameworks like ISO/IEC 42001 and the NIST AI RMF?
Governance frameworks require you to know what AI you operate, what risk it carries and how it is controlled. AI-SPM produces exactly that evidence on an ongoing basis — inventory, risk register, control coverage and change history — which is what turns a governance framework from a document into something you can demonstrate.
?What AI security risks should enterprises monitor continuously?
The recurring ones are shadow AI adoption, prompt injection and jailbreaks, sensitive data entering prompts and embeddings, over-privileged agents and non-human identity sprawl, insecure model supply chains, vector store exposure, unvalidated model output, and gaps in AI logging that leave the SOC without visibility.
?When should an organization start an AI-SPM program?
The practical trigger is the first moment AI reaches production data or customers — or the first enterprise security questionnaire that asks how you govern AI. Starting while the estate is small keeps discovery cheap and lets guardrails be designed in rather than retrofitted.
?How does Indrasol help with AI-SPM?
Indrasol runs the full loop: discovery and inventory, AI security risk assessment, governance and compliance alignment, agent and model security review, supply chain review, prioritization in business terms, and hands-on remediation support — then establishes the cadence that keeps posture current as AI adoption grows.
Ready to Put a Number on Your AI Risk?
A discovery call is a short, practical conversation about where AI is running in your environment, what it can reach, and what a measurable posture program would look like for your organization.
No obligation · A practical conversation about your AI security priorities
What the discovery call covers
Your current AI footprint
Where AI is already running across your business units, and where inventory gaps are most likely to sit.
The risks worth sequencing first
A short, prioritized view based on what your AI systems can reach — not a generic risk list.
What a posture program would take
Scope, sequence and effort for your environment, so you can judge whether it belongs on this year's roadmap.
