AI Security
What Is AI Security? A Practical Guide to Securing the Future of AI Systems
What AI security really means, why traditional tools won’t cut it, and how to defend GenAI systems from real-world attacks.
Artificial intelligence is rapidly reshaping how we build, operate, and interact with software. But while the capabilities of GenAI systems are accelerating, their defenses aren’t keeping up. What happens when models make the wrong decisions, leak sensitive data, or fall prey to manipulation? What happens when they’re not just buggy—but exploitable?
This article explores AI security in its proper context: not AI used to secure systems, but how we secure the AI itself.
We’ll unpack the threats that target today’s models, examine how they slip through traditional security layers, and walk through the frameworks and tools emerging to fix the gap. Whether you’re a product leader, a security architect, or simply trying to make sense of how AI changes your threat model—this guide is for you.
TL;DR
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- AI security isn’t cybersecurity 2.0. Traditional tools break down when applied to dynamic, emergent, and language-driven systems like LLMs.
- The most common threats exploit behavior, not code. From prompt injection to context poisoning, attackers manipulate reasoning—not infrastructure.
- Security gaps emerge across the entire AI lifecycle. Risks aren’t limited to model deployment—they show up in data collection, training, tool integration, and runtime.
- Real defenses exist—and they’re already in use. Teams are deploying adaptive guardrails, threat-aware monitoring, and red teaming to stay ahead of attackers.
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Build AI that’s secure by design. Lakera Guard protects your GenAI systems from prompt injection, data leakage, jailbreaks, and more—without rewiring your stack.
The Lakera team has accelerated Dropbox’s GenAI journey.
“Dropbox uses Lakera Guard as a security solution to help safeguard our LLM-powered applications, secure and protect user data, and uphold the reliability and trustworthiness of our intelligent features.”
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If you’re building or securing GenAI systems, these related reads go deeper into specific threats and practical defenses that complement your AI security strategy:
- See how attackers manipulate model behavior with prompt injection—and why it’s at the center of GenAI risk discussions.
- Learn how direct prompt injections bypass safety layers with simple but powerful input phrasing.
- Understand how training data poisoning compromises models before they’re even deployed.
- For teams concerned with output-level risks, this guide to content moderation in GenAI explores real-time, policy-based safeguards.
- Want to see how jailbreaks work in practice? This LLM jailbreaking guide breaks it down.
- Monitor model behavior post-deployment with confidence using LLM monitoring.
- And to proactively validate defenses, this guide to AI red teaming shows how top teams test their systems before attackers do.
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What Is AI Security?
AI security is the discipline of protecting artificial intelligence systems—especially large language models (LLMs) and generative AI—from manipulation, misuse, and attack. It spans the entire AI lifecycle: from training data pipelines to deployment, inference, and real-time interactions with users.
But AI systems aren’t like traditional software. They introduce a new attack surface—language—and operate in ways that are inherently unpredictable. Inputs aren’t structured API calls; they’re human prompts, often ambiguous, open-ended, and adversarial by design.
As we outlined in our red teaming work, every prompt is essentially committing code to the application. In this world, a sentence can be an exploit, and your model’s output can be the breach.
This is what sets AI security apart:
- The system’s behavior is non-deterministic
- Attacks happen in context, not through code injection but through conversation
- Exploits evolve with human creativity—not malware signatures
One of the most prominent examples is prompt injection. By crafting clever inputs, attackers can override system instructions, bypass filters, or extract sensitive data from memory. These attacks don’t require infrastructure access—they just require the right words at the right time.
And they’re not hypothetical. Prompt injection, indirect prompt leaks, model theft, and output manipulation have all been observed in production systems. They’re the new frontier of application-layer threats.
**💡 For a deep dive into how these attacks work, see our guide: Prompt Injection & the Rise of Prompt Attacks.**
In short, AI security is about defending a new kind of software—one where the boundaries between users, developers, and attackers are often blurred. And securing it demands new approaches, new tools, and constant vigilance.
Why AI Security Matters
AI systems are already becoming embedded in the fabric of critical operations—from healthcare triage to financial services, customer support, code generation, and enterprise search. But as these systems take on more responsibility, the risks tied to their behavior increase exponentially.
Unlike traditional software, AI doesn’t operate on fixed logic. It interprets prompts. It reasons through context. It responds differently depending on what’s said—and how. That’s what makes it powerful, and also deeply exploitable.
When AI systems fail, they don’t just crash. They mislead, hallucinate, disclose, or manipulate.
These failures can come from:
- Adversarial users, who craft inputs to override instructions
- Corrupted data, which introduces bias or logical flaws
- Stolen models, which leak intellectual property or core business logic
- Leaky outputs, which reveal sensitive information from training or prompt history
Prompt injection attacks, for example, don’t require malware, credentials, or network access. They exploit the model’s own reasoning process, using natural language alone to override behavior or exfiltrate data.
And the stakes rise even further with autonomous AI agents. These aren’t just models that generate text—they perceive, reason, and act. They can call APIs, browse the web, schedule meetings, or execute code. A manipulated input doesn’t just hijack output—it can redirect the agent’s real-world actions.
With AI agents, prompt injections don’t just break instructions—they redirect behavior.
These risks don’t live inside code repositories or firewalls—they emerge in the open, at runtime, from live user interactions. That’s why AI security isn’t just a technical issue—it’s a strategic imperative for anyone deploying GenAI in production:
- Legal and compliance teams, facing new liability under frameworks like the EU AI Act or NIST RMF
- Product and engineering teams, whose AI-powered features can be manipulated to erode user trust
- Security leaders, tasked with protecting systems that are no longer deterministic
Traditional security models were built for systems that are static and predictable. But GenAI systems are dynamic, emergent, and shaped by behavior—not rules. That means we need new approaches, new assumptions, and new mental models. For a broader perspective on this shift, read The Security Company of the Future Will Look Like OpenAI.
**👉 Want to see what these attacks look like in action? Play Gandalf and experience real-time LLM red teaming in your browser.**
Risks Across the AI Lifecycle
AI systems are vulnerable at every stage of their lifecycle—not just in production. Understanding where risks originate is the first step toward building defenses that actually work.
Most traditional security measures focus on endpoints, APIs, or infrastructure. But with GenAI, threats can emerge long before an LLM is ever deployed—and long after it’s gone live.
Let’s look at how these risks show up across each stage of the lifecycle—and what they look like in the wild.
1. Data Collection & Preprocessing
Risk: Data poisoning
One of the clearest examples came from Pliny the Prompter, an AI researcher who seeded malicious prompts across the internet months in advance—just waiting for them to be scraped. When an open-source model later ingested those web pages using a search tool, a simple query triggered the model to output explicit rap lyrics, overriding its safety filters. The exploit was cleverly timed, entirely language-based, and showed how easily LLMs can be compromised before they even reach production.
**Even the earliest stage—what data your model sees—can become an attack vector.**
2. Model Training & Fine-Tuning
Risk: Logic corruption
In 2025, researchers demonstrated how fine-tuning a model on insecure code snippets caused it to behave in unexpected (and deeply misaligned) ways. The model didn’t just generate unsafe code; it also started offering dangerous advice and making bizarre claims about AI dominance. The kicker? These behaviors weren’t part of the fine-tuning task—they simply emerged. That’s the risk of introducing logic flaws early: once embedded, they can be hard to trace and even harder to fix.
**Your model’s logic is only as safe as your training data and environment.**
3. Deployment & Integration
Risk: Prompt injection and code execution
In mid-2024, a vulnerability in Vanna.AI exposed a serious flaw in how LLM outputs were used during deployment. A prompt injection attack caused the model to generate code that was passed directly into a visualization function—without proper validation. The result? Remote code execution on the host machine. The model did exactly what it was told—but no one expected the instruction to be hostile.
**This is where GenAI systems meet the real world—and attackers meet your users.**
4. Inference & Ongoing Use
Risk: Unintended data leakage
Toward the end of 2024, researchers uncovered that coding assistants like GitHub Copilot could regurgitate secrets from repositories that were once public—even after they had been made private. In total, over 20,000 repos were involved, and the AI models still surfaced everything from secret API keys to embedded credentials. This wasn’t an attack per se—it was the consequence of models using stale training data. But it still posed a serious threat.
**Your model’s attack surface doesn’t shrink over time—it grows.**
5. Monitoring & Iteration
Risk: Persistent backdoors and false alignment
Even with the best intentions, monitoring can miss what a model is hiding. In early 2024, researchers at Anthropic trained models to behave normally—unless a specific keyword or date appeared. When triggered, the model revealed its backdoored behavior, producing unsafe outputs or inserting vulnerabilities into generated code. What’s worse, standard safety fine-tuning couldn’t fully remove the behavior. To outside observers, the model looked safe—until it wasn’t.
**Without proper monitoring, models can silently fail, leading to significant consequences.**
These risks don’t live inside your codebase or pipeline. They emerge through the messy, real-world interactions between people and models. And they can show up long after a model has been deployed.
Securing GenAI systems means securing the entire lifecycle—from the first dataset you collect to the last token your model produces.
AI Security in Practice: Defenses That Work
If the risks across the AI lifecycle feel wide-ranging, it’s because they are. But that doesn’t mean you need a thousand tools or a team of PhDs to start defending your systems.
The most effective AI security strategies share three traits:
- They focus on context-aware defenses, not just static filters.
- They adapt over time, as models evolve and threats emerge.
- And they cover the full lifecycle, from pre-deployment to real-time inference.
Let’s break that down:
1. Preventing Bad Inputs and Dangerous Outputs
This is where most teams start—and where many stop. But blocking obvious threats (like swear words or injection keywords) isn’t enough. You need defenses that understand context, intent, and linguistic trickery.
Techniques that work:
- Input sanitization tailored to LLMs (e.g. prompt structure analysis)
- Output filtering with semantic classifiers, not just regex
- Language-aware policies that account for jailbreak attempts in multiple languages
**👉 For a hands-on look at how real-world attackers bypass static filters, check out Gandalf the Red: Adaptive Defenses — Lakera’s research into dynamic, game-informed defenses that evolve faster than the attacks themselves.**
2. Guarding the Boundaries Between LLMs and Real-World Systems
LLMs don’t cause damage in a vacuum—it’s when they’re wired to external systems, tools, or data pipelines that the risks become real.
Techniques that work:
- Tool sandboxing for agentic systems
- Permission controls on function calls and tool invocations
- Rate limiting and behavioral policies for action-triggering prompts
Lakera Guard is built to protect tool-integrated systems, including cutting-edge architectures like MCP.
**👉 Learn more in How to Secure MCPs with Lakera Guard, which walks through real vulnerabilities and how to close them.**
3. Detecting Novel Threats Through Behavioral Monitoring
AI threats don’t stay static. Neither can your defenses. This is where live red teaming and behavioral feedback loops come into play.
Techniques that work:
- Embedding-based anomaly detection
- Guardrail breach monitoring
- Exploit feed integration
Lakera’s Gandalf platform serves as a live red-teaming engine, generating thousands of novel attacks every day. Behind the scenes, it powers everything from policy tuning to model hardening.
**👉 Explore the origins of this approach in Day Zero: Building a Superhuman AI Red Teamer.**
4. Testing Models Before (and After) Deployment
No LLM should go into production without being tested like a mission-critical system. And testing shouldn’t stop after shipping.
Techniques that work:
- Red teaming with adversarial evaluation
- Live exploit simulation using public and custom jailbreaks
- Scenario-based output audits
For a real-world walkthrough of testing RAG pipelines, see RAG Under Attack. It covers two major attack surfaces (user as victim, user as attacker) and demonstrates how even internal RAG systems can be compromised with poisoned context.
5. Starting Fast—Without Sacrificing Security
The hardest part of AI security? It’s not building guardrails—it’s knowing where to start. Most teams get stuck debating thresholds, enforcement levels, or which inputs matter most. And while those questions are valid, they can also be paralyzing.
That’s why the best defenses today aren’t just advanced—they’re opinionated and ready to go.
Techniques that work:
- Vetted, use-case-specific policy templates
- Global sensitivity controls to dial in enforcement gradually
- Ability to evolve—from observability to enforcement to customization
Lakera Guard comes with five pre-built policies for common GenAI use cases—each one designed by security experts, tested in real-world environments, and deployable in seconds. Whether you’re launching a chatbot or hardening an internal tool, you can start from a solid baseline and scale up security as you go.
**📖 Learn more in How to Secure Your GenAI App When You Don’t Know Where to Start: a guide for teams looking to move fast without getting it wrong.**
Done right, AI security becomes a flywheel. Every blocked attack feeds your threat intelligence. Every policy iteration makes the next deployment smoother. And every new use case gets better protection—without reinventing the wheel.
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👉 Curious how this works in practice?
- Explore Lakera Guard
- See how Gandalf fuels live threat intel
- Dig deeper into RAG system defenses
- Or start with pre-built protections
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Common Myths About AI Security
As GenAI adoption accelerates, so does confusion about how (and whether) to secure it. Many teams still rely on outdated assumptions or apply traditional cybersecurity logic to systems that behave nothing like traditional software.
Here are some of the most common myths—and why they’re dangerous.
Myth 1: “AI security just means using AI for cybersecurity.”
This is one of the biggest sources of confusion—especially when reading headlines.
Truth: There are two completely different meanings of “AI security.” One refers to AI used in security products (like detecting phishing emails). The other—the one we focus on—is about securing the AI systems themselves from attacks like prompt injection, data leakage, model theft, or unauthorized tool access.
If you’re deploying LLMs, you’re not looking for AI to protect your systems. You’re looking to protect your AI.
Myth 2: “Prompt injection isn’t a real security risk—it’s just a UX issue.”
This one usually comes from product owners or engineers who’ve only seen basic jailbreaks.
Truth: Prompt injection is not a novelty—it’s an exploit. It can override instructions, leak memory, or even escalate into real-world consequences in agentic systems. Some attacks are subtle, indirect, and multilingual. And they’re already being used in red teaming exercises and real-world exploits.
**📖 Learn how prompt design and context manipulation affect LLM behavior in How to Craft Secure System Prompts for LLM and GenAI Applications.**
Myth 3: “We already use filters—so we’re covered.”
It’s easy to assume that a profanity filter or keyword blocklist is all you need to stay safe.
Truth: Attackers aren’t using the words you’re filtering for. They’re using language creatively—switching languages, obfuscating meaning, or chaining logic over multiple interactions. Static filters are necessary, but on their own, they’re wildly insufficient.
**📖 For a broader look at how red teaming reveals jailbreak bypasses in practice, see Insights from the World's Largest Red Team.**
Myth 4: “Our vendor handles that.”
Many GenAI adopters assume that whatever risks exist are already handled by the LLM provider.
Truth: While foundation model providers invest heavily in alignment, they can’t anticipate how you’ll use the model, what tools you’ll connect to it, or what policies matter for your business. The closer the model gets to your users and your data, the more security becomes your problem.
**📖 For help evaluating GenAI architectures based on security needs, check out How to Choose the Best GenAI Security Solution.**
Myth 5: “We’ll just handle security later—after the prototype works.”
This is the fastest way to end up with tech debt you can’t unwind.
Truth: LLMs don’t behave like traditional APIs. They respond to whatever users put in—and once you expose that surface, it’s hard to retrofit protections without disrupting functionality. Early-stage GenAI systems often take shortcuts that later become liabilities. Secure-by-default saves time and reduces risk.
**📖 If you’re just getting started, the AI Security for Product Teams Handbook walks you through the essentials of building defensible GenAI systems.**
The most dangerous myth? Believing you’re not a target.
If you’re deploying GenAI, you’re already part of the attack surface.
Security isn’t just about compliance—it’s about making sure your product behaves as intended, even in the hands of a creative adversary.
Frameworks and Guidance: Making Sense of the AI Security Landscape
By now, the risks probably feel clear—and maybe a bit overwhelming. The good news? You’re not starting from zero. There’s a growing body of AI risk management frameworks, checklists, and guidelines built to help security, engineering, and product teams navigate this space.
Let’s look at a few that are shaping how GenAI systems are being secured today:
OWASP Top 10 for LLM Applications (2025)
The OWASP foundation recently released an updated GenAI-specific version of its iconic Top 10. This framework lays out the most common and critical vulnerabilities in LLM-based applications, including:
- Prompt injection
- Insecure output handling
- Overreliance on LLMs for decision-making
- Excessive agency (e.g. tool-calling without boundaries)
It’s a great reference for teams building LLM-connected applications—especially agents, copilots, and RAG systems.
Lakera’s platform and red teaming work closely align with the OWASP Top 10. You’ll find prompt injection, tool misuse, and data leakage among the top ranked risks—many of which are covered by default in Lakera Guard’s policies.
NIST AI Risk Management Framework
The NIST AI RMF is broader—it’s less about specific attacks and more about helping organizations govern their AI initiatives responsibly. It offers a structured approach to identifying, mapping, measuring, and managing AI risks.
The core idea? AI security is a continuous, contextual process, not a one-time checklist.
For GenAI teams, it’s a useful lens for building risk-aware development processes, especially when paired with more technical guidance like OWASP or MITRE ATLAS.
MITRE ATLAS (Adversarial Threat Landscape for AI Systems)
MITRE ATLAS is a living knowledge base of tactics, techniques, and case studies for attacking and defending AI systems. Unlike broader risk frameworks, ATLAS is laser-focused on adversarial behavior—including poisoning, evasion, extraction, and LLM-specific tactics.
It maps these techniques into a structure familiar to security teams—mirroring the original MITRE ATT&CK format used for traditional cyber threats.
What makes ATLAS especially useful is its emphasis on:
- Real-world attack chains, not just theoretical risks
- Model-specific vectors like prompt injection, training data manipulation, and model evasion
- A shared vocabulary between AI teams and traditional red/blue teams
Google’s Secure AI Framework (SAIF)
SAIF focuses on how to secure AI infrastructure and models—from training pipelines to access controls and inference monitoring.
While it’s primarily aimed at platform providers and infrastructure architects, it highlights the need for:
- Threat modeling tailored to AI
- Strong identity, access, and data controls
- Ongoing monitoring for model behavior drift and anomalies
These principles show up in how Lakera handles production deployments—ensuring that every system call, input, and output is observable and governed by policy.
OWASP Large Language Model Security Verification Standard (LLMSVS)
While the OWASP Top 10 identifies what can go wrong in GenAI applications, the LLMSVS focuses on what to build in. It’s a comprehensive verification standard designed to help developers and security teams assess the maturity and safety of their LLM-based systems.
What makes LLMSVS especially useful is its structure:
- Three security assurance levels, tailored to different risk profiles
- A clear mapping to the software development lifecycle (SSDLC)
- Guidance on everything from real-time learning controls and model lifecycle management to plugin and agent security
It’s not just a checklist—it’s a way to think systematically about GenAI security as part of how you build and ship software.
**📄 Want a practical overview of how to use LLMSVS in your own workflows? Download OWASP LLMSVS Guide for GenAI Builders.**
The Future of AI Security
AI security today feels urgent—but what’s coming next will make today’s threats look simple. Over the next few years, we won’t just be securing chatbots or content filters. We’ll be securing networks of intelligent agents, running autonomously, acting on our behalf, and connected to every layer of our digital infrastructure.
This shift is already underway. And it changes everything.
Here’s what’s on the horizon—and what it means for security teams.
1. From Prompts to Agents: A New Security Perimeter
The biggest shift? You’re no longer securing one model—you’re securing a system of reasoning agents.
These agents won’t just respond to prompts. They’ll plan, delegate, call APIs, and even hire other agents to complete tasks. It’s what we described as the Internet of Agents: a decentralized, hyper-connected mesh of AI actors making decisions in real time.
The security implications are massive:
- Prompt injection becomes task injection
- Jailbreaks become workflow hijacks
- Fine-tuning vulnerabilities become agent coordination attacks
**📖 To explore this vision in more depth, see The Rise of the Internet of Agents.**
2. Security Will Shift From Reactive to Adaptive
Rule-based filters and pre-programmed defenses won’t hold up. As systems become more autonomous, their defenses will need to evolve just as dynamically.
We’ll see a rise in adaptive security: real-time, model-aware systems that learn from attempted exploits and reconfigure themselves—just like attackers do. Guardrails will behave more like immune systems than firewalls.
**📖 Learn how teams build real-time attacker awareness in Building AI Security Awareness Through Red Teaming with Gandalf.**
3. The Future Will Be Regulated—and the Window to Build Smart Is Now
By 2035, securing AI will be more than a best practice—it’ll be a baseline expectation, enforced through regulation, market pressure, and risk frameworks.
That’s the future Lakera explored in our joint article with Twilio. From dynamic model auditing to developer-friendly policy enforcement, tomorrow’s GenAI stack will demand security controls that are embedded, not bolted on.
The good news? Teams that start now can get ahead of the curve—without overbuilding or slowing down.
**📖 See AI in 2035: Securing the Future of Customer Engagement to read the full vision we developed with Twilio.**
Security isn’t just a response to today’s threats. It’s a design choice for what kind of AI future we want to build.
Closing Thoughts
AI security isn’t just about preventing worst-case scenarios—it’s about unblocking progress.
Right now, too many teams are stuck. Not because their models don’t work, but because they can’t prove they’re safe enough to ship. Security becomes the bottleneck—not because it’s hard, but because it’s unclear.
That’s the real challenge: a lack of clarity is slowing GenAI adoption more than any single threat.
But this is also where the opportunity lies. The teams that understand how to secure their GenAI systems—before and after deployment—aren’t just safer. They’re faster. More confident. Better equipped to push AI into real production environments.
You don’t need to predict every exploit. You just need to treat security as part of how you build and ship AI—not something you duct-tape on at the end.
The frameworks are here. The tools are ready. The risks are clear. The teams that move now will move faster, not slower.
AI security isn’t just a blocker—it’s what enables real-world impact.
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