When an AI system misbehaves, from leaking sensitive data to producing manipulated outputs, the instinct across the industry is to reach for familiar tools: patch the issue, run another red team, test more edge cases.
But AI doesn’t fail like traditional software.
It doesn’t crash, it adapts. It doesn’t contain bugs, it develops behaviors.
That difference changes everything.
AI introduces an entirely new class of risk that cannot be mitigated with the same frameworks, controls, or assumptions that have defined cybersecurity for decades. To secure AI, we need more than traditional defenses. We need a shift in mindset.
The Illusion of the Patch
In software security, vulnerabilities are discrete: a misconfigured API, an exploitable buffer, an unvalidated input. You can identify the flaw, patch it, and verify the fix.
AI systems are different. A vulnerability isn’t a line of code, it’s a learned behavior distributed across billions of parameters. You can’t simply patch a pattern of reasoning or retrain away an emergent capability.
As a result, many organizations end up chasing symptoms, filtering prompts or retraining on “safer” data, without addressing the fundamental exposure: the model itself can be manipulated.
Traditional controls such as access management, sandboxing, and code scanning remain essential. However, they were never designed to constrain a system that fuses code and data into one inseparable process. AI models interpret every input as a potential instruction, making prompt injection a persistent, systemic risk rather than a single bug to patch.
Testing for the Unknowable
Quality assurance and penetration testing work because traditional systems are deterministic: the same input produces the same output.
AI doesn’t play by those rules. Each response depends on context, prior inputs, and how the user frames a request. Modern models also inject intentional randomness, or temperature, to promote creativity and variation in their outputs. This built-in entropy means that even identical prompts can yield different responses, which is a feature that enhances flexibility but complicates reproducibility and validation. Combined with the inherent nondeterminism found in large-scale inference systems, as highlighted by the Thinking Machines Lab, this variability ensures that no static test suite can fully map an AI system’s behavior.
That’s why AI red teaming remains critical. Traditional testing alone can’t capture a system designed to behave probabilistically. Still, adaptive red teaming, built to probe across contexts, temperature settings, and evolving model states, helps reveal vulnerabilities that deterministic methods miss. When combined with continuous monitoring and behavioral analytics, it becomes a dynamic feedback loop that strengthens defenses over time.
Saxe and others argue that the path forward isn’t abandoning traditional security but fusing it with AI-native concepts. Deterministic controls, such as policy enforcement and provenance checks, should coexist with behavioral guardrails that monitor model reasoning in real time.
You can’t test your way to safety. Instead, AI demands continuous, adaptive defense that evolves alongside the systems it protects.
A New Attack Surface
In AI, the perimeter no longer ends at the network boundary. It extends into the data, the model, and even the prompts themselves. Every phase of the AI lifecycle, from data collection to deployment, introduces new opportunities for exploitation:
- Data poisoning: Malicious inputs during training implant hidden backdoors that trigger under specific conditions.
- Prompt injection: Natural language becomes a weapon, overriding instructions through subtle context.
Some industry experts argue that prompt injections can be solved with traditional controls such as input sanitization, access management, or content filtering. Those measures are important, but they only address the symptoms of the problem, not its root cause. Prompt injection is not just malformed input, but a by-product of how large language models merge data and instructions into a single channel. Preventing it requires more than static defenses. It demands runtime awareness, provenance tracking, and behavioral guardrails that understand why a model is acting, not just what it produces. The future of AI security depends on integrating these AI-native capabilities with proven cybersecurity controls to create layered, adaptive protection.
- Data exposure: Models often have access to proprietary or sensitive data through retrieval-augmented generation (RAG) pipelines or Model Context Protocols (MCPs). Weak access controls, misconfigurations, or prompt injections can cause that information to be inadvertently exposed to unprivileged users.
- Malicious realignment: Attackers or downstream users fine-tune existing models to remove guardrails, reintroduce restricted behaviors, or add new harmful capabilities. This type of manipulation doesn’t require stealing the model. Rather, it exploits the openness and flexibility of the model ecosystem itself.
- Inference attacks: Sensitive data is extracted from model outputs, even without direct system access.
These are not coding errors. They are consequences of how machine learning generalizes.
Traditional security techniques, such as static analysis and taint tracking, can strengthen defenses but must evolve to analyze AI-specific artifacts, both supply chain artifacts like datasets, model files, and configurations; as well as runtime artifacts like context windows, RAG or memory stores, and tools or MCP servers.
Securing AI means addressing the unique attack surface that emerges when data, models, and logic converge.
Red Teaming Isn’t the Finish Line
Adversarial testing is essential, but it’s only one layer of defense. In many cases, “fixes” simply teach the model to avoid certain phrases, rather than eliminating the underlying risk.
Attackers adapt faster than defenders can retrain, and every model update reshapes the threat landscape. Each retraining cycle also introduces functional change, such as new behaviors, decision boundaries, and emergent properties that can affect reliability or safety. Recent industry examples, such as OpenAI’s temporary rollback of GPT-4o and the controversy surrounding behavioral shifts in early GPT-5 models, illustrate how even well-intentioned updates can create new vulnerabilities or regressions. This reality forces defenders to treat security not as a destination, but as a continuous relationship with a learning system that evolves with every iteration.
Borrowing from Saxe’s framework, effective AI defense should integrate four key layers: security-aware models, risk-reduction guardrails, deterministic controls, and continuous detection and response mechanisms. Together, they form a lifecycle approach rather than a perimeter defense.
Defending AI isn’t about eliminating every flaw, just as it isn’t in any other domain of security. The difference is velocity: AI systems change faster than any software we’ve secured before, so our defenses must be equally adaptive. Capable of detecting, containing, and recovering in real time.
Securing AI Requires a Different Mindset
Securing AI requires a different mindset because the systems we’re protecting are not static. They learn, generalize, and evolve. Traditional controls were built for deterministic code; AI introduces nondeterminism, semantic behavior, and a constant feedback loop between data, model, and environment.
At HiddenLayer, we operate on a core belief: you can’t defend what you don’t understand.
AI Security requires context awareness, not just of the model, but of how it interacts with data, users, and adversaries.
A modern AI security posture should reflect those realities. It combines familiar principles with new capabilities designed specifically for the AI lifecycle. HiddenLayer’s approach centers on four foundational pillars:
- AI Discovery: Identify and inventory every model in use across the organization, whether developed internally or integrated through third-party services. You can’t protect what you don’t know exists.
- AI Supply Chain Security: Protect the data, dependencies, and components that feed model development and deployment, ensuring integrity from training through inference.
- AI Security Testing: Continuously test models through adaptive red teaming and adversarial evaluation, identifying vulnerabilities that arise from learned behavior and model drift.
- AI Runtime Security: Monitor deployed models for signs of compromise, malicious prompting, or manipulation, and detect adversarial patterns in real time.
These capabilities build on proven cybersecurity principles, discovery, testing, integrity, and monitoring, but extend them into an environment defined by semantic reasoning and constant change.
This is how AI security must evolve. From protecting code to protecting capability, with defenses designed for systems that think and adapt.
The Path Forward
AI represents both extraordinary innovation and unprecedented risk. Yet too many organizations still attempt to secure it as if it were software with slightly more math.
The truth is sharper.
AI doesn’t break like code, and it won’t be fixed like code.
Securing AI means balancing the proven strengths of traditional controls with the adaptive awareness required for systems that learn.
Traditional cybersecurity built the foundation. Now, AI Security must build what comes next.
Learn More
To stay ahead of the evolving AI threat landscape, explore HiddenLayer’s Innovation Hub, your source for research, frameworks, and practical guidance on securing machine learning systems.
Or connect with our team to see how the HiddenLayer AI Security Platform protects models, data, and infrastructure across the entire AI lifecycle.
