Artificial intelligence has crossed a threshold from “tool” to active participant in both cyber offense and defense, and that shift is quietly reshaping enterprise risk in ways our traditional posture assessments don’t fully capture. Adversaries are now exploiting AI systems themselves (via data poisoning, model extraction, and prompt injection) while also weaponizing AI to accelerate classic intrusions (credential theft, phishing, lateral movement) a one‑two punch that can slip past controls built for pre‑AI threats. To keep pace, enterprises need threat modeling and detection that span the AI lifecycle, integrated with SOC workflows, and tied to concrete mitigations that don’t just harden endpoints and identities, but also the models, data pipelines, and agents we’ve embedded into our core business processes.
From “Attackers Using AI” to “Attacks on AI”: Two Intertwined Risk Planes
Security teams must distinguish between: (1) adversaries using AI to scale and personalize intrusions; and (2) adversaries attacking enterprise AI systems (LLMs, vector stores, model registries, and inference services). Microsoft’s 2025 Digital Defense Report notes that threat actors apply generative AI to speed phishing and discovery, pushing incident velocity beyond human response cycles identity attacks rose, hybrid ransomware spiked, and automation became table stakes for defenders. In parallel, NIST’s AI Risk Management Framework and adversarial ML taxonomy emphasize that AI stacks are uniquely exposed across training, fine‑tuning, and deployment, requiring security patterns that go beyond ordinary app hardening.
A Common Language for AI Threats: MITRE ATLAS and OWASP for LLMs
Enter MITRE ATLAS the adversarial AI companion to ATT&CK which catalogs tactics and techniques mapped to real‑world case studies (e.g., poisoning, model extraction, supply‑chain compromises) and provides mitigations you can operationalize alongside your SOC’s ATT&CK‑based detections. ATLAS complements the OWASP Top 10 for LLM Applications, which surfaces risks like prompt injection, system‑prompt leakage, excessive agency (unsafe tool use), and data/model poisoning that are now routine in enterprise GenAI rollouts. Together, they supply a shared vocabulary and a set of controls that integrate with existing detection/response playbooks.
How AI Helps Adversaries Evade Your Posture
The most visible shift is speed and scale: AI‑assisted operators automate reconnaissance, generate convincing lures in any language or persona, and test defenses iteratively until something breaks. Microsoft reports >100 trillion security signals processed daily and warns that attackers are adapting in real time, compressing the dwell time defenders once relied on; industry coverage echoes the surge in AI‑assisted phishing, identity abuse, and hybrid cloud ransomware. Meanwhile, surveys and government analyses highlight increases in deepfake‑enabled fraud and prompt‑based manipulations targeting enterprise AI applications.
Example 1 GenAI‑Augmented Social Engineering
Threat groups fuse cloned voices, synthetic video, and tailored narratives to execute high‑value BEC and help‑desk impersonation often with “human‑in‑the‑loop” AI assistants that adapt during the call. CrowdStrike’s 2025 report details deepfake‑driven fraud and social profiles fabricated at scale, illustrating how AI shaves weeks off preparation cycles and raises the baseline competence of less skilled actors.
Example 2 Discovery and Evasion at Machine Speed
Agentic scripts can enumerate misconfigurations across cloud tenants, mutate payloads, and route around guardrails. Microsoft warns that the “speed of AI” is now a factor in both offense and defense, urging teams to move from prevention‑only to resilience (detect, contain, recover) and to instrument identity, patching, and response as live KPIs, not static policies.
How Adversaries Attack the AI You Deploy
When enterprises adopt GenAI and MLOps, they add new choke points to the attack surface: data pipelines, model registries, embeddings stores, orchestration layers, and tool‑calling agents. ATT&CK gives us the “classic” map of adversary behavior; ATLAS extends it to attacks that specifically target AI. Four families matter most for posture:
Prompt Injection & Excessive Agency
Indirect prompt injection (from web/RAG inputs, PDFs, or email threads) can cause LLM agents to exfiltrate secrets, invoke dangerous tools, or violate authorization boundaries. OWASP’s LLM Top 10 and Cloudflare’s explainer document both direct and indirect patterns, and mitigation hinges on robust output handling, least‑privilege tool APIs, allowlist‑based retrieval, and out‑of‑band policy checks.
Data & Model Poisoning
Attackers seed training/fine‑tuning or embedding corpora with backdoors that activate only under specific triggers, yielding “clean” evaluations but malicious behavior in production. NIST’s AML taxonomy classifies poisoning across the AI lifecycle; recent research shows supply‑chain backdoors (e.g., TransTroj) can persist through downstream fine tuning with high success. For posture, this is akin to dependency tampering for models.
Model Extraction, Inversion, and Side‑Channels
Models can leak intellectual property or training secrets via query attacks, inversion, or even physical side‑channels on accelerators and endpoints. Surveys and systematizations (USENIX, arXiv) catalog practical extraction pathways; RAND outlines dozens of weight‑theft vectors spanning insiders, cloud orchestration, and supply chain. Expect regulators and customers to ask how you protect model weights and sensitive training data.
AI Infrastructure Exploits
AI frameworks and orchestration layers are new software stacks with web‑exposed APIs. Case studies like “ShadowRay” showed how default‑open job APIs yielded token theft, DB access, and cloud compute abuse; MITRE’s ATLAS brief highlights this as a real campaign against AI workloads not just a lab demo.
The Cloud Posture Angle: Why Traditional CSPM Alone Isn’t Enough
Classical CSPM looks for misconfigurations (exposed buckets, weak IAM, unpatched services). With AI, you must overlay AI‑specific controls: provenance for training/fine‑tuning data, model registry attestation, signing and SBOM‑like manifests for models, agent/tool authorization boundaries, and runtime evaluation of model outputs before they trigger side effects. ATLAS data shows most mitigations map to existing controls (identity, network, secrets, change control) but must be re‑scoped to the model lifecycle; OWASP’s LLM risks add application‑security depth that posture tools should ingest as rules and detections.
What “Good” Looks Like: A Threat‑Informed, AI‑Aware Posture Program
- Start with Threat Modeling that Uses ATLAS + ATT&CK Together
Map business‑critical AI use cases (chat assistants, RAG on sensitive data, agentic workflows) to ATLAS techniques (e.g., poisoning, prompt injection, model theft) and then chain them to ATT&CK steps (initial access, credential access, lateral movement) to derive detections, controls, and tabletop exercises. MITRE’s ATLAS and CISA’s ATT&CK mapping practices make this executable for SOCs. - Treat AI as a Supply Chain
Require model provenance (source, license, training summary), signed artifacts, vulnerability and policy scanning for models and datasets, plus quarantine and staged promotion for every model update. Research on backdoored PTMs and industry write‑ups on model‑supply poisoning underscore the need for a curated registry with enforced policy gates. - Build Guardrails Around Agents and Tools
Separate LLM reasoning from action: enforce out‑of‑band authorization checks for tool calls, use structured outputs, sanitize inputs/outputs, and restrict external retrieval to allowlists; align with OWASP LLM Top 10 controls for prompt injection, output handling, and excessive agency. Instrument execution sandboxes and set rate/impact limits on “actuator” tools (tickets, payments, IAM changes). - Secure Model Weights and Sensitive Data
Follow RAND’s guidance to inventory where weights live, who can access them, and which channels (network, storage, CI/CD, debug ports) could leak them. Consider HSMs or GPU enclaves for the crown jewels; pilot results from secure enclave evaluations show promise but also operational caveats plan for telemetry and incident response, not just encryption. - Adversarial Testing Is Not Optional
Run continuous LLM red teaming (manual + automated) against production‑like stacks: simulate prompt injection via RAG, jailbreaks, PII exfiltration, tool abuse, and data poisoning scenarios. Google’s AI Red Team and recent survey guides detail realistic TTPs; integrate failing cases into policy updates and guardrails just as you would any other regression. - Telemetry, Detections, and Response For AI
Add detections for AI‑specific anomalies: sudden vector‑store drift, unusual tool‑use sequences, spikes in refusal circumventions, or unexpected model swaps in CI/CD. Microsoft’s guidance stresses resilience metrics (MFA coverage, patch latency, mean time to containment) that should now include model pipeline KPIs (data freshness, guardrail hit rates, red‑team fail rates).
Case‑Based Posture Scenarios You Should Rehearse
RAG Prompt Injection Exfiltration: External content carries hidden instructions; the assistant retrieves and inadvertently leaks secrets or executes tickets with business impact. Practice detection (content provenance logs, abnormal tool calls), containment (quarantine connectors), and customer comms. Map to OWASP LLM01/LLM05 and ATLAS input manipulation.
Model Registry Backdoor: A poisoned “minor update” reaches staging; tests pass, but a latent trigger exfiltrates credentials when a specific query pattern occurs. Require signed models, staged promotion, shadow inference, and canary monitoring for backdoor activation. Use ATLAS techniques and NIST AML taxonomy to define controls.
Frontier Weights Theft Attempt: Insider plus external actor targets your inference fleet snapshot, CI system, and S3 buckets. Counter with segmented access, egress controls, tamper‑evident storage, and posture monitoring for enclaves/HSMs; prepare a legal and crisis plan given the value and sensitivity of stolen weights.
Metrics That Matter (and Drive Executive Buy‑In)
Report AI posture alongside your cloud metrics: % models with signed artifacts; % RAG connectors on allowlists; # of successful/blocked prompt‑injection attempts per week; red‑team fail rate and mean time to guardrail update; % of sensitive datasets with provenance attestations; % of weights stored in enclaves/HSMs; and AI incident MTTD/MTTR. These measures tie directly to risk narratives CFOs and CISOs already recognize from Microsoft’s macro trends and NIST’s governance principles.
Bottom Line for Enterprise Leaders
AI doesn’t just widen the attack surface it creates a parallel attack surface whose failure modes are statistical, emergent, and sometimes invisible until the trigger fires. Treat AI posture as a first‑class security program: combine ATLA informed threat modeling, OWASP LLM controls, and resilience‑centric operations from the cloud world you already know. The organizations that thrive will be those that can teach and guide their own security teams and customers through this transition turning posture insights into clear workflows, measurable guardrails, and fast feedback loops that keep models (and people) honest.
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