A single compromised admin session. That’s all it took for one AI startup to lose access to their model registry, feature store, and six months of training logs. The attacker got in through a phishing email that bypassed MFA because the team was using SMS codes instead of hardware keys.
This isn’t a cautionary tale about AI-specific vulnerabilities. It’s about something more fundamental: AI-native companies are, above all, companies. They work with people, devices, clouds, and budgets. They face the same traditional threats that have plagued every generation of technology – phishing, ransomware, malware, compromised accounts, BEC fraud, misconfigured infrastructure.
But here’s what makes AI-native companies different: the consequences are amplified, and the attack surface is broader. More automation means more integration points. Agents orchestrate across external tools and APIs. RAG systems pull from diverse data sources. Models are downloaded from public registries. Every connection is a potential vector, and many of these vectors aren’t visible in traditional security dashboards.
It’s tempting to assume that “the model itself is secured” by the provider – OpenAI, Anthropic, whoever you’re calling. This assumption shifts attention away from your architecture and processes toward a black box that feels safe by default. Meanwhile, the real risks accumulate in the seams: how you collect, transform, and index data; how you connect agents with tools; what permissions you grant to services and people; how you manage secrets and keys; whether you can even answer the question “what changed and when?”
Add another layer of complexity: startups rarely have dedicated security teams. They work under intense market pressure. They deploy changes rapidly. “Shadow AI” experiments surge as teams test new models and tools independently. Vulnerabilities reach production not because engineers are careless, but because the guardrails don’t exist yet.
The result? Traditional security controls – identity management, privilege separation, backup procedures, monitoring – become even more critical, not less. Ignore them, and you’re not building an AI company. You’re building a fast product that won’t become a lasting business.
In this article, we break down the five key risks for AI-native companies and share practical guidance on how to manage them.Let’s dive in.
1. Traditional Threats Still Matter – More Than Ever
Traditional threats remain the first line of risk because “AI-native” still means clouds, CI/CD pipelines, data stores, people, and vulnerable configurations – the same vectors targeted by phishing, malware, and ransomware.
The theft of a single administrative session can open access to container registries, model stores, feature databases, or logs that capture sensitive prompt interactions. Misconfigured object storage can expose training datasets or prompt templates. Poor IAM policies can enable privilege escalation through Kubernetes orchestrations or analytics platforms.
The realistic response isn’t exotic:
- Enforce strong multi-factor authentication with FIDO2 for sensitive roles
- Maintain strict separation between production and testing environments
- Implement disciplined backup and recovery processes
- Deploy mature email security
- Build a team culture where people recognize suspicious emails, texts, deepfake videos, and social engineering attempts
The AI difference? The consequences. A successful phishing attack doesn’t just unlock an inbox – it often provides access to artifacts that can compromise your entire product. One leaked credential can mean stolen model weights, poisoned training data, or access to customer query logs.
2. The Compromised AI Supply Chain
The second threat is particularly relevant for AI-native businesses: a compromised AI supply chain. Models, weights, datasets, containers, and libraries are routinely downloaded from public registries and catalogs – HuggingFace, GitHub, PyPI, Docker Hub – where malicious post-installation scripts, tampered serializations, or vulnerable base images are not uncommon.
The risk is amplified by the common practice of “testing directly in production” with community-sourced models. Sound hygiene starts with provenance and integrity: pinned versions and commit hashes, signed artifacts, and a maintained MBOM (Model Bill of Materials) and SBOM (Software Bill of Materials) so you know exactly what you’re running and where it came from.
Isolation is the next layer: separate environments for training and inference, minimal privileges for service accounts, no arbitrary network egress, and the use of an internal model registry where every artifact goes through attestation, automated security testing, and approval before deployment.
True maturity comes when these checks are integrated into CI/CD pipelines and no longer depend on an engineer’s good intentions or memory.
3. Data Poisoning – Training and Retrieval
The third threat is data poisoning, which can happen both during training and within knowledge retrieval systems (RAG). A few seemingly innocent examples can shift model behavior in sensitive scenarios. Backdoor techniques can insert triggers that unlock unwanted responses under specific conditions. Indirect prompt injections can sneak through indexed documents – PDFs, web pages, Markdown files – to alter the model’s or agent’s instructions when they’re retrieved and processed.
This risk is technically manageable if you treat data as critical code:
- Verify sources via cryptographic hashes and signatures
- Build pipelines that normalize and sanitize HTML and Markdown structures
- Configure RAG systems to read only from approved domains and storage buckets, never from arbitrary user-provided URLs.
Strong teams maintain “canary” documents – test cases designed to trigger specific unwanted behaviors – and run automated safety evaluations regularly. Monitor not just quality metrics but also embedding distributions and retrieval patterns, so you can answer “what changed and when?” with evidence, not guesswork.
4. Leakage of Secrets, IP, and Model Theft
The fourth risk involves leaks of secrets and intellectual property, as well as model extraction or distillation. In early-stage teams, it’s surprisingly easy for API keys to end up hardcoded in frontends, notebooks, or application logs. Temporary tokens become permanent. Service accounts get shared across teams and projects.
Meanwhile, public-facing inference APIs can be systematically probed to reproduce model behavior through repeated queries, or to extract training examples through membership inference attacks.
Defense starts with strict secret management:
- Use hardware security modules or cloud key vaults
- Enforce rotation policies
- Keep token lifetimes short
- Assign minimal scopes
And continue with architectural discipline:
- Serve inference only from backend services
- Separate keys by environment
- Use dedicated virtual networks
- Budget alerts to detect anomalous usage
When technical measures aren’t enough, legal and contractual protections around IP and data become essential. Clear artifact classification (public, internal, confidential) and data handling agreements with partners and vendors give you process and legal grounding to fall back on.
5. Regulatory Requirements: GDPR, AI Act, NIS2
The fifth area shifts the conversation from best practices to provable obligations. For AI-native companies, regulatory compliance- GDPR, the AI Act, NIS2, HIPAA, DORA and others – means mapping data processing within AI-specific workflows, conducting Data Protection Impact Assessments (DPIAs) where risks to individuals are high, and maintaining technical documentation for your systems: model versions, data provenance, known limitations, evaluation results, rollback procedures.
The AI Act introduces risk classification (minimal, limited, high, unacceptable), transparency requirements for users interacting with AI systems, and obligations for post-market monitoring. NIS2 adds organizational and technical security measures, supplier risk management, and strict incident reporting timelines.
The mature approach is to embed these controls directly into MLOps: automate compliance checks in CI/CD pipelines, generate audit-ready artifacts and logs as part of your standard workflow, and schedule regular audits and penetration tests that specifically target AI surfaces – prompt injection attempts, RAG poisoning, model inversion attacks.
Compliance becomes a feature of your engineering practice, not a quarterly scramble before an audit.
Conclusion: Security Makes Innovation Sustainable
AI doesn’t eliminate the fundamentals of cybersecurity – it amplifies the consequences of neglecting them.
When you strengthen traditional protections and adapt them for AI workloads, when you train your people to stay vigilant, when you treat models and data as critical versioned artifacts, when you tighten secret management, minimize API exposure, and turn regulatory obligations into engineering practices – you don’t slow down innovation.
You make it sustainable.
That’s the difference between a fast product and a lasting business.
Let’s talk about what that looks like for your organization. Book your free consultation with our team.
