How to Protect Your Organization from AI Attacks: A CISO’s 90-Day Playbook

To protect your organization from AI attacks, map your AI attack surface, enforce layered GenAI security controls (input, model, and output), align governance to NIST/ISO frameworks, operationalize AI-specific detection and incident response, and harden your AI supply chain. Treat AI like a new class of business-critical systems with dedicated controls, telemetry, and drills.

You already defend cloud, endpoints, identities, and apps at scale—now AI adds a fast-moving, opaque layer that reshapes risk in weeks, not years. AI-enhanced phishing, prompt injection, model/data poisoning, and supplier misconfigurations raise stakes across your enterprise stack. Executive expectations are high; board questions are sharper. The good news: the security fundamentals you trust still work—if you adapt them for AI systems.

This guide gives you a pragmatic, 90-day blueprint to secure GenAI and AI-assisted workflows without stalling innovation. You’ll learn how to map and shrink your AI attack surface, deploy layered defenses against prompt injection and data exfiltration, align to NIST and ISO/IEC 42001, operationalize MITRE ATLAS in your SOC, and pressure-test your AI supply chain. You’ll also see how AI Workers can augment your security team—so you do more with more—without replacing the people who keep your enterprise safe.

Why AI attacks are different—and why your current controls aren’t enough

AI attacks are different because models execute untrusted instructions, transform sensitive data, and integrate with tools autonomously—creating new paths for compromise beyond traditional app and API threats.

For a CISO, that translates into four urgent realities: 1) inputs become code (prompt injection can redirect model behavior), 2) outputs can be weaponized (insecure output handling may trigger downstream tools), 3) models are new assets with unique telemetry gaps, and 4) suppliers control critical parts of your stack (foundation models, gateways, vector DBs). According to industry analysts, AI-enhanced malicious attacks have already become a top emerging risk for enterprises, reflecting how quickly threat actors adapt attack chains to GenAI-enabled environments.

The stress you feel—unknown-unknowns, audit pressure, unclear ownership—is the byproduct of rapid adoption. Teams spin up proof-of-concepts, connect models to data lakes, and wire agents to production tools. Meanwhile, classic guardrails (DLP, API gateways, SAST/DAST) only partially apply. The result: expanding attack surface with blind spots in data lineage, model behavior, third-party dependencies, and human-in-the-loop governance. Treat this as a new class in your program with dedicated discovery, controls, and response.

Map and reduce your AI attack surface fast

To map and reduce your AI attack surface, inventory every place models or AI services touch your data, tools, and users—then classify risks by data sensitivity, tool permissions, and exposure to untrusted content.

Start with a 360° discovery sprint:

• Catalog GenAI apps, model providers, vector stores, embeddings pipelines, agents, orchestration layers, and AI gateways.
• Trace data flows: what sensitive data prompts include, what the model stores, where outputs go, and which tools/credentials agents can use.
• Score exposure: is the model reading the public web, email, tickets, PDFs, or partner content you can’t sanitize?
• Prioritize by blast radius: executive comms, customer data, finance approvals, code repos, and production runbooks come first.

Set design-time guardrails now: define allow/deny lists for tools an AI agent can call; restrict data domains for prompts; and require human-in-the-loop for actions that can move money, change access, or alter production configs.

What is an AI attack surface, and how do you bound it?

An AI attack surface is the sum of model inputs, outputs, tools, data sources, and integrations that adversaries can influence or exploit; you bound it by limiting untrusted inputs, restricting tool permissions, segmenting data, and enforcing least privilege at the agent layer.

Scope each AI system as you would a microservice: define explicit ingress (who/what can prompt), egress (where outputs can go), secrets (what keys/models it can access), and trust zones (which data is in-bounds). Treat prompts and retrievals as regulated data flows with tagging and policy enforcement.

How do you inventory shadow AI and undocumented model integrations?

You inventory shadow AI by scanning logs and repositories for API keys, SDK imports, model calls, embedding ops, and by surveying business units for AI pilots tied to KPIs or vendor contracts.

Augment with procurement and expense data to catch unmanaged SaaS AI tools; correlate with identity and secrets managers to find unmanaged keys; and inspect CI/CD for model registry or inference endpoints. Establish a lightweight intake process to convert shadow pilots into governed workloads.

How often should you review AI data flows and permissions?

You should review AI data flows and permissions at least quarterly, and on any material change to models, tools, data sources, or business processes using AI.

Automate drift detection where possible and require re-approval for new tool bindings, expanded data domains, or higher-privilege actions. Tie reviews to change management so AI risk posture updates travel with releases.

Deploy layered defenses for GenAI and LLMs

To deploy layered defenses for GenAI, combine input sanitization and policy checks, model-level guardrails, and output validation with isolation so malicious prompts can’t trigger sensitive actions.

Implement a three-layer pattern:

1. Pre-model (input) controls: sanitize and classify prompts; strip/neutralize untrusted HTML/JS/hidden text; enforce policy on PII/PHI/secrets; throttle and size-limit inputs.
2. In-model controls: system prompts with safety policies; retrieval whitelists; context segmentation; content filters; tool permission gating; rate limiting.
3. Post-model (output) controls: validate and sanitize responses; require structured outputs; run “intent filters” before executing actions; sandbox automations.

Anchor your control set to community guidance like the OWASP Top 10 for LLM Applications, and bake mitigations into dev templates so every new AI feature inherits the shield.

How do you stop prompt injection and indirect prompt injection?

You stop prompt injection with layered content defenses, strict tool permissioning, retrieval whitelists, output validation, and deny-by-default execution of external instructions.

Adopt a zero-trust stance toward content: anything a model reads (web pages, emails, PDFs, wiki) is untrusted. Use isolation, pattern-based detectors, and intent classifiers to flag adversarial instructions, and gate tool calls behind policy. See evolving mitigations from Google’s security team on layered defenses against prompt injection: Mitigating prompt injection attacks.

How do you prevent data exfiltration via AI tools and outputs?

You prevent data exfiltration by enforcing prompt-level DLP, redaction, and tokenization; restricting retrieval to approved indexes; and blocking sensitive fields from being synthesized or exported.

Instrument inference gateways to inspect prompts and outputs for secrets and regulated data; version and approve retrieval indexes; and watermark/label AI outputs so downstream systems can apply the right handling rules.

What guardrails should run at inference time in production?

At inference time, you should run input classifiers, policy enforcement, safety/risk scoring, output validators, and tool-call approval checks—before any action executes.

Consider defense-in-depth aligned to vendor guidance; for example, Microsoft and others recommend layered mitigations and runtime checks for indirect injections and tool misuse. Build these into your platform SDKs so teams can’t bypass them.

Govern AI with policies and controls aligned to NIST and ISO/IEC 42001

To govern AI effectively, define a clear AI policy set, map controls to existing frameworks (NIST AI RMF and ISO/IEC 42001), and require control evidence through your normal GRC lifecycle.

Start with ownership: designate product and security control owners for every AI workload. Then codify policy areas: acceptable use, data classification in prompts/contexts, model/provider selection, retrieval governance, tool permissioning, human-in-the-loop approvals, and logging/retention. Align this to the NIST AI RMF Generative AI Profile for risk functions (Govern, Map, Measure, Manage) and to ISO/IEC 42001 for a formal AI management system.

Make it real by integrating with release gates: require threat modeling for AI features, model cards for transparency, SBOM/MBOM equivalents for AI components, and privacy reviews for data use. Track exceptions and compensating controls through your existing GRC tooling.

What policies do CISOs need for AI right now?

CISOs need policies for AI acceptable use, data in prompts/contexts, model/provider selection, retrieval sources, tool/agent permissions, human approvals, logging/retention, red-teaming, and incident response.

Each policy should specify minimum controls, evidence requirements, and audit cadence. Treat AI like payments or PII processing: no ambiguity about who can do what, with which data, and under what approvals.

How do you map NIST AI RMF to your existing security program?

You map NIST AI RMF by aligning Govern/Map/Measure/Manage functions to your secure SDLC, third-party risk, privacy, and detection/response processes—so AI-specific risks ride your proven rails.

For example, Map = AI asset inventory and data lineage; Measure = safety/robustness testing and control efficacy; Manage = runtime enforcement and incident playbooks. Use your GRC system to host AI control objectives and evidence.

Operationalize AI threat detection, red teaming, and incident response

To operationalize AI defense, extend your SOC runbooks with AI telemetry, adopt MITRE ATLAS for threat modeling, and run AI-specific red team exercises that validate detection and response.

Instrument your inference gateways, retrieval pipelines, and agent orchestrators to produce security-grade logs: prompts, tools invoked, data sources used, safety scores, and blocked actions. Build detections for anomalous tool use, unusual data retrievals, repeated safety-policy hits, and model DoS patterns. Use the MITRE ATLAS knowledge base to map adversary tactics and ensure coverage across reconnaissance, poisoning, evasion, and impact.

How do you adapt MITRE ATLAS to your SOC?

You adapt MITRE ATLAS by mapping its AI-specific TTPs to detections on your inference and agent logs, then validating end-to-end through purple teaming.

Create a coverage matrix: ATLAS technique → telemetry source → detection rule → response step. Fold it into your existing MITRE ATT&CK views to preserve a single command language for analysts.

How do you run an AI red team exercise with business value?

You run an AI red team exercise by testing real business workflows (e.g., finance approvals, customer responses) against prompt injection, tool abuse, and data exfiltration scenarios—with measurable findings tied to control improvements.

Design adversarial content sets (emails, web pages, PDFs) and attempt indirect injections; measure how often guardrails prevent tool execution; and quantify residual risk. Prioritize fixes that reduce blast radius, not just block single payloads.

What belongs in AI-specific incident response playbooks?

AI incident playbooks should include model isolation steps, key revocation, prompt/context snapshotting, retrieval index rollback, provider escalation, and customer comms tailored to AI incidents.

Define fast paths to disable high-risk tools, block untrusted sources, and revert to human-only operation modes. Pre-approve decision trees so you’re never debating authority during an AI-driven incident.

Secure the AI supply chain and your vendor landscape

To secure the AI supply chain, evaluate providers and gateways for security posture, test models for poisoning/drift, and harden your CI/CD and model registries like any high-value software asset.

Third-party exposure is now normal: foundation models, embedding services, vector DBs, RAG pipelines, and agent frameworks often sit outside your direct control. Apply tiered due diligence: demand audit logs, model isolation options, content safety stacks, tool permission controls, and SOC 2/ISO attestations that actually cover AI features, not just generic SaaS.

How should you assess AI vendors and gateways?

Assess AI vendors and gateways by verifying runtime policy enforcement, prompt/output inspection, tool/agent permissioning, data residency/retention controls, and exportable security logs for your SIEM.

Ask for red team reports against OWASP LLM Top 10 risks and require integration tests in your environment. Prefer vendors with documented mitigations for prompt injection and secure tool use.

How do you test for model poisoning, drift, and unsafe behavior?

You test for poisoning, drift, and unsafe behavior with staged datasets, adversarial prompts, safety benchmarks, and continuous evaluation pipelines that measure robustness over time.

Gate production promotion on passing safety and performance thresholds; snapshot retrieval indexes; and monitor for shifts in outputs that could indicate upstream data tampering.

How do you protect CI/CD, model registries, and secrets?

Protect CI/CD and registries by enforcing signed artifacts, role-based access, secret scanning, and policy-as-code that blocks deployments lacking AI control evidence.

Treat prompts, system instructions, and retrieval configurations as code: review, version, sign, and track changes. Rotate provider keys and restrict them by workload and environment.

From static controls to AI Defenders: turning AI Workers into your security force multiplier

The shift is from static gates to living defenses: AI Workers can continuously discover shadow AI, review prompts for sensitive data, triage risky outputs, and simulate red-team attacks—so your experts focus on judgment, not drudgery.

EverWorker embraces “Do More With More.” Instead of replacing analysts, AI Workers shoulder the repetitive load: scanning repos for model calls and leaked keys, checking inference logs for anomalous tool use, validating retrieval sources, and assembling evidence for audits. If you can describe the task, you can create AI Workers in minutes and plug them into your security data fabric. As your environment evolves, your AI Workers evolve too—codifying your playbooks and scaling your coverage without inflating headcount. Explore how teams go from idea to employed AI worker in 2–4 weeks, and why AI Workers are the next leap in operational resilience. As platform capabilities grow (EverWorker v2), you extend the same governance: policy-first, evidence-backed, human-supervised.

Build AI security mastery across your team

Security wins when capability spreads. Upskill architects, app teams, and SOC analysts on GenAI attack paths, OWASP LLM mitigations, and AI-specific response—so the whole organization moves faster, and safer, together.

What to do next

Over the next 90 days, execute the fundamentals: complete your AI asset inventory, enforce input/model/output controls, align governance to NIST/ISO, light up AI telemetry in your SIEM, and run a targeted red-team exercise. Then operationalize: automate continuous discovery, risk scoring, and evidence collection with AI Workers; expand coverage to critical workflows; and mature playbooks for AI incidents. You’re not starting from scratch—you’re extending a strong program to a new class of systems. Move with confidence.

FAQ

What is an AI attack in practical terms?

An AI attack is any tactic that manipulates or exploits AI systems—such as prompt injection, data/model poisoning, model denial-of-service, insecure tool use, or output hijacking—to gain unauthorized access, exfiltrate data, or cause harmful actions.

How do I prevent prompt injection without blocking productivity?

You prevent prompt injection by filtering untrusted inputs, constraining retrieval to approved sources, gating tool calls, and validating outputs—while enabling safe patterns (structured prompts, templates, human approvals) that preserve speed for legitimate work.

Should we block public GenAI tools outright?

You should replace blanket blocks with governed alternatives: provide approved AI tools with DLP, logging, and policy enforcement, and restrict high-risk use cases—so business teams stay productive while sensitive data stays protected.

Which frameworks should I anchor to for AI governance?

Anchor to the NIST AI RMF (and its GenAI profile) for risk functions and to ISO/IEC 42001 for an AI management system; use OWASP LLM Top 10 for application-layer threats and MITRE ATLAS for adversary TTPs.

Where can my team learn more about AI application risks?

Review the OWASP Top 10 for LLM Applications for common risks and mitigations, and study Google’s latest guidance on layered defenses against prompt injection: Mitigating prompt injection attacks. For formal governance, see NIST’s Generative AI Profile and ISO/IEC 42001.

External resources referenced: OWASP Top 10 for LLM Applications, NIST AI RMF Generative AI Profile, MITRE ATLAS, ISO/IEC 42001, Google Security: Mitigating prompt injection. For deeper platform thinking, see our perspectives on AI Workers and how to create AI Workers in minutes.