A CISO’s Playbook for Managing Agentic Risk

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When AI Agents Become Insiders: A CISO’s Playbook for Managing Agentic Risk

 

CISOs are being asked to do something that rarely goes well in security: move fast and prove it’s safe. Across many organizations, leadership wants AI - exceptionally autonomous “agentic” capabilities - embedded into products, operations, and decision-making right now. That urgency creates a very real squeeze on security teams: compressed procurement timelines, rushed reviews, and a growing backlog of “is this AI app actually safe for our use case?” assessments.

What makes this wave different from prior tech shifts is that AI isn’t just another application - it can become a new kind of identity in the enterprise. As AI agents gain permissions to retrieve data, trigger workflows, and take actions across systems, they start to resemble insiders:persistent, capable, and sometimes over-privileged. The “superuser problem”emerges when agents are granted broad access that can be chained across sensitive applications without clear visibility or approvals, turning convenience into a high-impact control failure.

At the same time, the attack surface is evolving. Prompt injection and “tool misuse” vulnerabilities can convert a helpful agent into an autonomous insider under adversarial control - approving transactions, changing records, deleting backups, or exfiltrating critical data.

And once attackers gain a foothold, the new “high ground”may be the internal LLM itself - queried for answers, procedures, and pathways that accelerate lateral movement and privilege escalation.

This article unpacks these AI-era challenges through theCISO lens - speed vs. assurance, agent identities and access, adversarial manipulation, and operational readiness - and then lays out practical ways to manage them: least-privilege design for agents, strong governance and approvals, continuous monitoring and anomaly detection, and a roadmap that starts with safer use cases before expanding autonomy.

Below is guidance mapping the AI risks noted to practical actions aligned with ISO/IEC 42001 (AI Management System / AIMS) and ISO/IEC27001 (Information Security Management System / ISMS).

  • Speed vs. assurance: CISOs are pressured to deploy AI quickly, compressing procurement and security review cycles.
  • AI agents as a new “insider threat”: agents can look like “digital employees” with persistent access, and - if over-permissioned - become a “superuser” that can chain actions across systems.
  • Prompt injection/tool misuse → real-world actions: attackers can manipulate an agent to approve transactions, delete backups, or exfiltrate data.
  • “AI doppelganger” risk: delegating executive approvals to agents can create new fraud paths (e.g., contracts/wire approvals).
  • Internal LLM as the new “high ground”: once inside, attackers may query internal LLMs to accelerate lateral movement and privilege escalation.
  • Immature security patterns: AI innovation is outpacing security controls, so “secure by design and monitoring” is essential.

The rest of this article translates the above risks into anISO-aligned management approach.

How ISO 42001 and ISO 27001 fit together for AI risk

ISO/IEC 42001 (AIMS)

ISO/IEC 42001 specifies requirements for establishing, implementing, maintaining, and continually improving an AI management system - i.e.,organization-wide governance for responsible development/provision/use of AI systems.

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It includes AI-specific governance areas such as policy, internal organization, lifecycle, data, impact assessment, and third-party relationships (represented in Annex A control objectives).

ISO/IEC 27001 (ISMS)

ISO/IEC 27001:2022 provides the information security management framework and control set that protects confidentiality/integrity/availability - highly relevant because agentic AI introduces new identities, new pathways to sensitive data, and new failure modes.
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The 2022 revision’s Annex A has 93 controls grouped into four themes:organizational, people, physical, and technological.

Practical takeaway:
Use ISO 42001 to govern what AI is allowed to do and how you manage it acrossits lifecycle, and use ISO 27001 to implement security controls that prevent,detect, and respond to misuse.

ISO-aligned guidance to manage the specific risks

1) Put “AI adoption pressure” under formal governance and change control

Risk: Rushed procurement/security checks and uncertainty about whether AI apps are safe for real use cases.

ISO 42001-aligned actions (AIMS):
  • Define the scope of your AIMS: which AI systems, agents, LLM platforms, and business processes are in scope.
  • Establish an AI policy and AI objectives with a clear risk appetite (especially for autonomous actions). (Annex A objective area: “Policies related to AI”.)
  • Create a tiering model for AI use cases (e.g., Tier 0 = no autonomy; Tier 3 = autonomous actions in production) and require formal approvals by tier.

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ISO 27001-aligned actions (ISMS):
  • Treat agent deployments like any other high-risk change:
       
    • mandatory security review gates
    • rollback plans
    • pre-production testing evidence
    • documented risk acceptance where needed
Audit-friendly evidence
  • AI policy and AI risk appetite statements
  • AI use-case tiering matrix
  • change approval records for “agent goes live” events

2) Build a combined AI risk assessment and AI impact assessment process

Risk: New attack surface (prompt injection/tool misuse) and broader organizational risk from autonomous insiders.

ISO 42001-aligned actions (AIMS):
  • Run a repeatable AI risk assessment process and a related AI impact assessment (impact focused on external stakeholders).
  • Use threat modeling specifically for agent workflows (tools, connectors, permissions, memory, retrieval sources).

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ISO 27001-aligned actions (ISMS):
  • Integrate AI risks into your existing ISMS risk register:
       
    • define likelihood/impact
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    • define treatment plans
      •  
    • track residual risk acceptance
Audit-friendly evidence:
  • AI risk register entries (prompt injection, tool misuse, over-privilege, data exfil)
  • documented impact assessments for higher-risk AI systems (especially those affecting customers/employees)

3) Treat every AI agent as a “real identity” with least privilege and firm boundaries

Risk: “superuser problem” and privileged agents becoming the insider threat.

ISO 42001-aligned actions (AIMS)
  • Define ownership and accountability for each agent (business owner, technical owner, and security approver). (Annex A objective area: “Internal organization” / “Resources”.)
  • Require agents to be explicitly designed with a bounded purpose (what they are allowed to do) and bounded operating conditions (where/when they can do it). (Annex A objective area: “Use of AI systems”.)
ISO 27001-aligned actions (ISMS):
  • Implement identity and access management patterns that assume agents are privileged     insiders:
       
    • unique service identities per agent (no shared keys)
    • least privilege (minimum tool permissions, minimum data access)
    • time-bound access (just-in-time credentials, short-lived tokens)
    • separation of duties (agents can draft; humans approve)
    • frequent access reviews for agent identities
Audit-friendly evidence:
  • agent inventory (agent → owner → tools → permissions → data store
  • periodic access review records
  • privileged access management logs for agent accounts

4) Engineer defenses for prompt injection and “tool misuse” as first-classsecurity requirements

Risk: A single crafted prompt/tool vulnerability can turn an agent into an autonomous attacker.

ISO 42001-aligned actions (AIMS):
  • Make prompt injection/tool misuse part of the AI system lifecycle controls: design → build → test → deploy → monitor. (Annex A objective area: “AI system life cycle”.)
  • Define acceptable sources of instructions and data (e.g., “untrusted web content can never override system instructions”). (Annex A objective area: “Data for AI systems” / “Use of AI systems”.)
ISO 27001-aligned actions (ISMS):
  • Apply secure engineering controls to agent architectures:
       
    • tool allowlists (agents may only call approved tools/endpoints)
    • parameter validation on tool calls (block dangerous commands)
    • sandboxing/isolation for high-risk tools (e.g., code execution)
    • outbound network egress controls for agents
    • logging of prompts, tool calls, and data access
Audit-friendly evidence:
  • secure design docs for agents (tool allowlist, isolation model)
  • prompt injection test results / red-team reports
  • monitoring dashboards and alert rules for anomalous tool usage

5) Prevent “AI doppelganger” approvals with strong human oversight and non-repudiation

Risk: Agents who are used to “approve”transactions/contracts, such as an executive, could create fraud and legal risk.

ISO 42001-aligned actions (AIMS):
  • Classify “approvals, payments, contract sign-off, M&A decisions” as high-impact AI uses requiring explicit safeguards and oversight (Annex A objective area: “Assessing impacts of AI systems”).
  • Ensure interested parties understand when AI is involved in decisions or recommendations (Annex A objective area: “Information for interested parties”).
ISO 27001-aligned actions (ISMS):
  • Enforce:
       
    • multi-factor authentication for human approvers
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    • dual approval for high-value actions (two-person rule)
      •  
    • cryptographic signing/approval workflows with immutable logs
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    • explicit bans on “agent final approval” for defined transaction classes
Audit-friendly evidence:
  • policy stating which actions can never be fully autonomous
  • workflow configs showing dual-approval enforcement
  • immutable audit trail records

6) Protect the internal LLM like a Tier-0 asset and stop it from becoming the attacker’s “high ground.”

Risk: Attackers may bypass the internal LLM to request guidance and expedite escalation/lateral movement.

ISO 42001-aligned actions (AIMS):
  • Define which sensitive knowledge the LLM is allowed to access via retrieval (e.g., runbooks, credentials processes, architecture diagrams), and apply     minimization. (Annex A objective area: “Data for AI systems”.)
  • Include “misuse by unauthorized internal actors” as a standard impact/risk scenario in assessments.
ISO 27001-aligned actions (ISMS):
  • Strong access control and segmentation:
       
    • restrict who can query internal LLMs
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    • restrict which corporate repositories can be indexed/retrieved
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    • DLP on inputs/outputs
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    • security monitoring for unusual query patterns (“tell me how to… dump credentials”)
Audit-friendly evidence:
  • Retrieval     allowlist/denylist rules
  • LLM access logs  anomaly detection reports
  • data classification and indexing rules for LLM knowledge bases

7) Manage third-party AI/agent platforms with supplier controls andlifecycle monitoring

Risk: Security is lagging behind model innovation; vendors and tools evolve rapidly.

ISO 42001-aligned actions (AIMS):
  • Apply Annex A objective area for third-party and customer relationships:
       
    • require clarity on data usage (training, retention)
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    • require transparency on model updates that could change behavior
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    • require incident reporting obligations
ISO 27001-aligned actions (ISMS)
  • Formal supplier risk assessments and contract controls:
       
    • security requirements in contracts
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    • breach notification SLAs
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    • right-to-audit / assurance reports
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    • Integration security review for connectors and plugins
Audit-friendly evidence:
  • vendor assessments specific to AI (data rights, logging, isolation, testing)
  • third-party monitoring / periodic reassessments

8) Operationalize monitoring and incident response for “rogue agents.”

Risk: Need to detect if an agent goes rogue and respond quickly.

ISO 42001-aligned actions (AIMS):
  • Establish monitoring and performance evaluation for AI systems, including incident learnings and continuous improvement (management-system lifecycle).
ISO 27001-aligned actions (ISMS):
  • Update incident response playbooks to include:
       
    • disabling agent identities (“kill switch”)
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    • revoking tool tokens
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    • isolating the agent runtime environment
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    • preserving prompt/tool-call logs as evidence
Audit-friendly evidence
  • IR playbooks covering agent compromise scenarios
  • tabletop exercise results for prompt injection/tool misuse incidents

 

A practical “starter set” you can implement in 30–60 days (ISO-aligned)

If you need to move quickly without losing control, implement these foundational deliverables first:

  1. AI system & agent inventory (what exists, who owns it, what it can access)
  2. Create levels (tiers) for AI use cases based on risk, and make a clear list of tasks where AI is not allowed to act on its own - for example, approving payments or signing contracts must always require a human.
  3. AI risk assessment and impact assessment templates aligned to your ISMS risk     register
  4. Agent identity standard: least privilege, unique identities, time-bound     credentials
  5. Tool governance gateway: allowlists, validation, logging, anomaly detection
  6. Supplier intake checklist for AI: data rights, retention, update notice, incident     SLAs
  7. Rogue-agent incident runbook and one tabletop exercise

These directly address the “agent as insider,” “superuser,”“prompt injection/tool misuse,” and “internal LLM high ground” risks addressed above.

 

Talk to us at KendraCyber to build your own Agentic AI Playbook!

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