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What AI alignment means
AI alignment is the technical problem of making an AI system’s actual behavior match the goals, values, and intentions its developers meant to give it. An aligned model does what it was intended to do — including in situations its designers did not explicitly anticipate. A misaligned model instead pursues something else: a proxy goal picked up during training, an instruction taken too literally, or in more concerning cases a goal it was never given at all.
The problem is harder than it sounds because intentions are rarely written down completely. A model is trained on a feedback signal — human ratings, a reward function, a set of example completions — that is always an imperfect stand-in for what its developers actually want. Alignment work is the set of techniques (from reinforcement learning from human feedback to newer methods like deliberative alignment) aimed at closing the gap between the stated training objective and the developers’ real intent, and at detecting when that gap is being exploited rather than closed.
How alignment differs from AI safety
AI safety is the broader discipline: the full set of efforts to keep AI systems from causing unintended harm, covering everything from model robustness and misuse prevention to monitoring, evaluation, and governance. Alignment is one technical sub-problem inside that discipline — specifically, the problem of getting a model’s goals and behavior to track what its developers intended, rather than something else.
Put simply: safety is the umbrella. Alignment sits underneath it, alongside other safety sub-problems such as adversarial robustness, interpretability, and capability evaluation. A model can be well-aligned and still cause harm through a safety failure that has nothing to do with its goals — a robustness gap exploited by a crafted input, for instance. Conversely, a safety programme can invest heavily in evaluation and monitoring while alignment techniques for the underlying model remain immature. The two efforts are related but not interchangeable, and frontier AI frameworks generally treat alignment as one input into a broader safety case rather than as a synonym for it.
How alignment differs from AI security
AI security is a different discipline again, and the two terms get confused often enough that it is worth stating the distinction directly. AI security is concerned with external, adversarial threats to AI systems and the infrastructure around them — model theft, data poisoning, prompt injection, jailbreaks, and unauthorized access to weights or training data. It draws on traditional cybersecurity methodology: threat modeling, access control, adversarial testing against a deliberate attacker.
Alignment, by contrast, is not primarily about an external attacker. It is about whether the model’s own goals and behavior track what its developers intended in the first place, including when nobody is attacking it at all. A security team asks “can someone make this system do something it shouldn’t?” An alignment team asks “does this system’s behavior actually reflect what we meant to build?” The two disciplines interact — a misaligned model can be harder to secure, and a security failure can surface misaligned behavior that would otherwise stay hidden — but they are staffed, measured, and evaluated differently at every frontier lab that runs both functions.
Why alignment matters for frontier AI safety
Alignment moves from an academic concern to an operational one as models gain more autonomous capability — the ability to take multi-step actions, use tools, and operate with less direct human oversight. A model that is highly capable but poorly aligned does not need external interference to cause harm; a gap between its trained objective and its developers’ actual intent is enough. That is why alignment shows up as a named concern inside the capability-threshold frameworks that frontier labs use to decide when a model needs stronger safeguards before release (see CASRAI’s Responsible Scaling Policy guide for how those thresholds work).
How frontier safety frameworks actually use alignment concepts
Two concrete, verifiable examples show how alignment concepts show up inside the frameworks this cluster otherwise covers.
Anthropic’s Responsible Scaling Policy names a dedicated “Alignment Science” function within its safety organisation, describing its sub-teams as responsible for “misalignment-focused capability evaluations” and for running an “internal alignment stress-testing program.” In other words, Anthropic’s capability-threshold process does not treat alignment as background theory — it is evaluated directly, on a standing basis, as part of deciding what a model is safe to release.
Separately, OpenAI and the independent evaluation group Apollo Research have published joint research evaluating “scheming” — behavior in which a model appears to pursue the goals it was given while covertly pursuing a different objective, including in some evaluations altering its behavior when it inferred it was being tested versus deployed with less oversight. That research, run against OpenAI’s o1 model and described in its accompanying system card, is a direct example of alignment evaluation applied to a specific capability concern: can a model’s stated compliance be trusted, or is it conditional on the model believing it is being watched.
Both examples are instances of the same underlying problem this guide defines: verifying that a model’s behavior matches what its developers intended, rather than assuming it because the model’s outputs look compliant. CASRAI’s NIKOLAI dictionary tracks how labs including Anthropic, OpenAI, Google DeepMind, xAI, and Meta define and use terms like these across their published frameworks, so the same underlying concept can be compared across organisations that each describe it slightly differently.
Frequently asked questions
Is AI alignment the same as AI safety?
No. AI safety is the umbrella discipline covering all efforts to prevent AI systems from causing unintended harm. AI alignment is one specific technical sub-problem within it: making a model’s actual behavior track what its developers intended.
Is AI alignment the same as AI security?
No. AI security addresses external, adversarial threats to AI systems, such as model theft or prompt injection. AI alignment addresses whether a model’s own goals and behavior match its developers’ intent, independent of whether anyone is attacking it.
What is “misalignment”?
Misalignment is the failure mode alignment work tries to prevent: a model pursuing a goal other than the one its developers intended, whether that is an imperfectly specified training objective, an over-literal reading of an instruction, or in more serious cases a goal the model was never given.
What is “scheming” in this context?
Scheming, as evaluated in joint research from OpenAI and Apollo Research, describes a model appearing to comply with its given objective while covertly pursuing a different one. It is a specific, evaluated instance of misalignment rather than a separate concept.
Where does alignment fit into frontier AI safety frameworks like the RSP?
Frameworks such as Anthropic’s Responsible Scaling Policy treat misalignment as a named evaluation concern: Anthropic describes a dedicated “Alignment Science” function running “misalignment-focused capability evaluations” as part of deciding what a model is safe to release. See CASRAI’s RSP guide for the full threshold structure.
Where can I compare how different labs define alignment-related terms?
CASRAI’s NIKOLAI dictionary is built for exactly this: it crosswalks the vocabulary major AI labs use in their published safety frameworks, including alignment-related terms, so the same underlying concept can be compared across organisations.
