Mapping the enterprise AI risk landscape and the controls required to operate AI securely at scale.
Unsanctioned tools in use with no visibility, approval process, or blocking strategy — the attack surface is already out of control.
Code generation tools — Claude Code, Codex, Cursor, Codeium — ship vulnerable patterns, hardcoded secrets, and supply-chain risks into production with no guardrails.
Agentic workloads run as isolated silos. No centralised view of tool calls, data access, memory state, or what happens during an incident.
No approved catalogue of agents or MCP servers. Teams connect arbitrary servers, granting unchecked tool access with no audit trail.
AI-specific attack paths — indirect prompt injection, privilege escalation via tool chains, data exfiltration through reasoning — are invisible to conventional scanners.
An AI Risk Management Framework orchestrates a structured assessment of the existing risk posture — mapping exactly where AI is running, what data it touches, and the blast radius of any compromise. You cannot govern what you haven't inventoried.
Inventory all AI tools, agents, APIs, and models — sanctioned and shadow.
Assess data sensitivity, permissions, and blast radius per workload.
Apply policies, approval workflows, and acceptable-use standards.
Continuous runtime telemetry, anomaly detection, and audit logging.
Red team findings drive policy updates and control hardening.
Create dedicated AI access groups. Only approved devices with MFA-satisfied sessions and explicit group membership can authenticate to AI providers.
Block AI app authentication from unmanaged or non-compliant endpoints using IDP device trust and authorization grant requirements.
DNS and proxy-layer blocking for uncategorised AI SaaS. Enforce an approved-only allowlist at the network perimeter — complements IDP controls.
OAuth flows like auth.openai.com bypass proxy controls entirely. IDP policy must cover the auth endpoints — not just the chat UI.
IDEs, CI pipelines, scripts
Product features, chatbots
Orchestration frameworks, custom agents
Approved integrations
Open-source or enterprise — depends on scale and compliance needs
Per-team token limits
Multi-model failover
Rate limits & quotas
Model by task & cost
Semantic deduplication
Full prompt telemetry
Compromised plugins downloaded from public repos introduce backdoors directly into developer environments — before a single line of code is written.
Developers connect arbitrary MCP servers granting AI unchecked access to filesystems, databases, and internal APIs — no approval, no audit.
AI coding agents inherit developer-level credentials — far exceeding least privilege. Any compromise of the agent is a compromise of the developer's full access.
There is no mechanism to observe what an AI coding assistant is actually doing — file reads, network calls, or token exfiltration go completely undetected.
AI tools can invoke shell commands, git operations, and package installs with no approval workflow, no rate limiting, and no audit trail.
Block unapproved IDE extensions via endpoint policy. Maintain a signed registry of approved skills. Continuously re-test for supply-chain compromise after every version update.
Scan MCP manifests for tool poisoning, prompt injection patterns, cross-origin redirects, and overpermissioned declarations before a developer can connect.
Pre/post-execution hooks enforce a baseline security process: secret detection, SAST check, dependency review, and approval gates before code is committed or deployed.
Containerise agent execution. Strict filesystem, network, and process namespacing. Ephemeral credentials — agents operate at minimum required permissions with no persistent state.
Agent swarms run as disconnected silos. During an incident there is no single answer to: what ran, what did it call, what did it access, and what changed?
Deploy agents as ephemeral containers. Existing primitives — network policies, seccomp, read-only filesystems, resource limits — become agent guardrails by default. No new tooling required.
Route all executions through a unified runtime platform — capturing tool call traces, memory snapshots, and inter-agent communication in one place.
Each agent receives a short-lived, scoped identity. Tool access is explicitly enumerated — no agent inherits ambient permissions from the host environment. Every invocation is logged against a named identity.
An unapproved MCP server declaring read_file, execute_sql, or send_email has been granted that access the moment a developer connects it. No approval, no audit, no revocation path.
A signed, version-controlled catalogue of approved MCP servers with documented capabilities and security review status. Unapproved servers blocked at endpoint and network layer.
Self-service submission portal → automated scanner (tool poisoning, prompt injection, SSRF) → security review gate → signed approval with expiry → continuous re-scan on version updates.
Standardised agent profiles define permitted tools, model, memory scope, and execution context. A well-governed catalogue reduces friction — developers get a fast path to approved, secure MCPs.
Injected prompt or poisoned context
Authorised, fully credentialled
send_email · read_db · execute_code
Data exfil · lateral movement
The real threat is not unauthorised tool invocation — it is authorised tool invocation with malicious intent. The agent is entitled. The attacker manipulates the reasoning chain so the agent weaponises its own permissions.
Malicious instructions hidden in user input, tool outputs, or retrieved documents.
Override the agent's objective mid-execution without triggering any safety check.
Legitimate, permitted tool calls — manipulated to produce harmful outcomes.
Chain innocuous calls across steps — no single request trips a rule, the sequence does.
Inject false context into agent memory — corrupting every future decision in that session.
Compromise one agent to pivot and escalate permissions across the wider swarm.
Map every AI workflow as a directed graph — attack paths emerge before a test runs.
Hundreds of injection variants, role confusion payloads, and chained tool abuse sequences per workflow.
Human adversarial thinking for novel chains — business logic abuse, cross-workflow pivots, compound scenarios.
Severity-rated findings with reproducible proof-of-concept — not a list, a full attack narrative.
Every finding becomes a runtime monitoring rule and a blue team response playbook.
Tool access scoped down, memory boundaries tightened, inter-agent trust explicitly restricted.
Accepted risks are owned and time-bounded. Red team cadence ensures nothing stays residual indefinitely.