Zone 1: Not Our Fight — Redirect Language for Out-of-Scope AI Topics
AI Model Safety
You hear:
- "We're trying to figure out how to comply with OMB's AI governance requirements — does Okta play a role in that?"
- "Our CISO wants assurance that the AI systems we're deploying are actually safe. Where do you fit?"
What this is: Model safety covers the behavioral guardrails, alignment techniques, and policy controls that prevent an AI system from producing harmful or unintended outputs.
Why they're raising it: Federal agencies are under real mandate pressure from OMB M-24-10 and NIST AI RMF to demonstrate responsible AI deployment, and "safety" is the word in every policy document landing on their desk right now.
Your redirect:
"That compliance pressure is real, and it's not going away. The behavioral side — guardrails, alignment, output controls — that lives with your AI platform team and whoever owns your AI governance framework. What we focus on is the identity layer underneath all of it: making sure every AI agent that touches your systems has a verified, auditable identity and only the access it's actually supposed to have. Different control, but one your auditors will ask about."
Prompt Injection Defense
You hear:
- "We're worried about adversaries manipulating our AI agents through crafted inputs. Is that something Okta addresses?"
- "What does Okta do about prompt injection?"
What this is: Prompt injection is an attack technique where adversarial inputs cause an AI agent to ignore its instructions or take unintended actions.
Why they're raising it: It's the AI vulnerability getting the most security leadership attention right now, and federal security architects are being asked by their CISOs whether deployed AI is hardened against it.
Your redirect:
"Prompt injection is a legitimate threat, and it's squarely in application security territory — the model itself, the input validation layer, the agent framework. Okta doesn't operate there. What identity governance does is limit the blast radius: if an agent gets manipulated, it can only act within the scope of what its identity is authorized to do. That's a real control, but it's a different layer than the injection defense itself."
LLM Red Teaming
You hear:
- "We're standing up a red team exercise for our AI systems. Does Okta have a role in that?"
- "Our security team wants to adversarially test the AI agents we're deploying. Where does identity fit?"
What this is: LLM red teaming is structured adversarial testing of AI systems to surface failure modes before they're exploited in production.
Why they're raising it: Post-EO, federal agencies with AI deployments are being asked to demonstrate they've stress-tested their systems — and security teams are sorting out who owns what in that exercise.
Your redirect:
"Red teaming the model itself is offensive security work — that belongs with your AI security team or a specialized vendor. Where we'd want to be part of that conversation is on the identity side: what identities do those agents hold, what access do they have, and does the red team have visibility into that attack surface? That's the piece we can speak to."
Training Data Poisoning
You hear:
- "We're concerned about adversaries corrupting the data our models are trained on."
- "How do we protect our training pipelines from tampering?"
What this is: Training data poisoning involves injecting malicious data into a model's training set to degrade its performance or introduce exploitable behaviors.
Why they're raising it: Agencies building or fine-tuning models on sensitive data are starting to treat the training pipeline as a critical asset — and they're not sure which security controls apply.
Your redirect:
"Protecting training data integrity is a data security and ML platform problem — not where Okta operates. The identity question that does connect is access governance for the pipeline itself: who can write to those data stores, who can trigger training runs, and is that access auditable? If that's a gap, it's worth a separate conversation."
AI Bias and Fairness
You hear:
- "We have civil rights obligations — how do we make sure our AI isn't making discriminatory decisions?"
- "Our legal team is asking about algorithmic accountability. Is that something you touch?"
What this is: AI bias and fairness covers the methods and governance structures used to detect and mitigate discriminatory outputs from AI systems.
Why they're raising it: SLED agencies running benefits, hiring, or law enforcement applications face real legal exposure, and federal agencies with civil rights mandates are under scrutiny from their OIG and oversight bodies.
Your redirect:
"Algorithmic accountability is model governance territory — the fairness audits, the bias testing, the documentation requirements. Okta doesn't own that. What identity governance does connect to is the human-in-the-loop question: for decisions that carry civil rights implications, who is authorized to review, override, or approve an AI recommendation? Making sure those humans are the right ones, with the right access, and a full audit trail — that's our lane."
AI Hallucination Mitigation
You hear:
- "What happens if our AI agent produces a wrong answer and acts on it? How do we prevent that?"
- "We're worried about agents making consequential decisions based on bad outputs."
What this is: Hallucination mitigation covers the model architecture, retrieval augmentation, and output validation techniques that reduce the frequency and impact of AI errors.
Why they're raising it: For agencies where AI agents are taking real actions — filing records, routing approvals, initiating transactions — the risk of an agent acting on a wrong output is a governance and liability concern, not just a technical one.
Your redirect:
"Reducing hallucinations is a model architecture problem — RAG design, output validation, confidence thresholds. Not where Okta operates. What identity does is bound what an agent can act on even when it's wrong: if an agent can only write to certain systems and trigger certain workflows, a bad output can't become a catastrophic action. That's a meaningful constraint on the damage surface, even if it's not the same as fixing the hallucination."
Zone 2: Bring In Your SE — Handoff Triggers Within Okta's Domain
OAuth/OIDC Protocol Questions
You hear:
- (Security architect): "How does Okta handle token binding for AI agents? Are you using DPoP?"
- (IT director): "What's the token lifetime for machine-to-machine flows, and can we configure refresh rotation?"
- (ISSO): "Does your OAuth implementation support PAR for high-assurance flows?"
Why this crosses the line: The buyer is asking for implementation-level answers on protocol mechanics — a wrong answer here gets corrected by the security architect in the room, and you don't recover from that.
What you say to the buyer:
"That's the right question to press on, and I want to make sure you get a precise answer rather than my approximation. Let me bring in my solutions engineer — they work these protocol specifics every day and can go through the configuration options with your security team directly."
What you send the SE: Use the universal template. Add the specific protocol question (DPoP, PAR, token lifetime, etc.) and note whether a security architect or ISSO is in the room — they'll want to go deeper than an IT director would.
SCIM and Provisioning Specifics
You hear:
- (IT director): "How does Okta handle provisioning for AI agents that aren't tied to a human identity?"
- (Integration engineer): "What does your SCIM 2.0 attribute mapping look like for service accounts?"
- (Procurement): "Can you provision and deprovision AI agent identities automatically, or is that a manual process?"
Why this crosses the line: Provisioning specifics depend on the buyer's existing directory structure, HR system, and agent framework — the SE needs to understand that environment before the answer means anything.
What you say to the buyer:
"Provisioning for non-human identities is one of those areas where the right answer really depends on your environment — your directory setup, what agent framework you're running, how your HR system connects. I want to bring in my SE so we can map that out accurately rather than give you a generic answer that doesn't fit your architecture."
What you send the SE: Use the universal template. Add the buyer's known directory or HR system and any agent framework they mentioned (LangChain, Microsoft Copilot Studio, custom-built, etc.).
XAA Architecture
You hear:
- "How does Okta actually establish identity for an AI agent — not a service account, an actual agent with its own identity?"
- "What's the trust model when one AI agent needs to call another? How does that work in your platform?"
- "We're building an agentic workflow — how does Okta fit into that architecture?"
Why this crosses the line: The buyer is asking for architectural commitments on a capability where availability status matters — and a wrong assumption here creates a credibility problem the SE will have to clean up.
Okta's Extended Autonomous Agent (XAA) framework is Early Access as of May 2026. Flag this to your SE before the call and confirm current GA status. Do not let architectural expectations form around a preview capability.
What you say to the buyer:
"Okta's approach to agent identity is a specific architectural model, and I want you to hear it from someone who can walk through the trust model and field your technical team's follow-up questions in real time. Let me get my SE on a call with you."
What you send the SE: Use the universal template. Add the agent framework or platform the buyer is building on. Flag that XAA is EA and confirm current availability status before the call — do not let the SE walk in assuming GA.
Competitive Technical Bake-offs
You hear:
- "We're also evaluating Microsoft Entra for this — how do you compare on non-human identity governance?"
- "SailPoint told us they handle AI agent identities natively. What's your response to that?"
- "We're running a proof of concept with three vendors. What should we be testing for?"
Why this crosses the line: Improvised competitive comparisons are where AEs do the most damage. A wrong technical claim about a competitor's capability gets corrected by the buyer's own research — and that's a credibility loss you can't walk back.
What you say to the buyer:
"I want to give you a comparison that holds up when your team digs into it. Let me bring in my SE — they've been through these evaluations and can help you build test criteria that surface the real differences rather than the marketing version."
What you send the SE: Use the universal template. Name the specific competitors and the exact capability the buyer wants compared. The SE needs to know what claim triggered the bake-off request, not just that a bake-off is happening. See Competitor Cards for positioning guidance — that belongs there, not here.
AI Identity Pricing and Packaging
You hear:
- (Procurement): "How are AI agent identities licensed? Per agent, per transaction, per tenant?"
- (IT director): "If we spin up a thousand agents for a workflow, does that change our contract?"
- (Budget owner): "We need to model out the cost before we take this to leadership. What are we looking at?"
Why this crosses the line: AI identity packaging is actively evolving. Quoting a model that's been updated since your last briefing creates a contract problem later — and in federal procurement, a number that goes into a budget model has a way of becoming a commitment.
What you say to the buyer:
"Pricing for AI agent identities is an area where I want to make sure you get current numbers — the packaging has been evolving and I don't want to hand you a model that's already out of date. Let me loop in my SE and our deal desk so you have something you can actually take to leadership."
What you send the SE: Use the universal template. Add the estimated agent volume if the buyer mentioned it and the budget cycle timeline. If they're building a model for leadership, the SE needs to know the deadline before the call, not during it.
Universal SE Handoff Template
Copy this into Slack, email, or your CRM note before the SE joins.
SE HANDOFF — [Account Name] | [Date]
WHO ASKED: [Name, title, role in the decision — IT director, security architect,
ISSO, procurement, budget owner]
THE QUESTION: [Exact language the buyer used. Don't paraphrase into category
labels — the SE needs to hear what the buyer actually said.]
WHAT I'VE ALREADY POSITIONED: [What you said about Okta before the handoff,
so the SE doesn't contradict you or repeat you.]
DEAL CONTEXT: [Stage, timeline, competing vendors, procurement constraints —
FedRAMP scope, contract vehicle, budget cycle.]
ANYTHING ELSE: [EA/GA sensitivity, specific personas in the room, prior
commitments made, political dynamics worth knowing.]Fill in every field. A handoff template with blanks is a handoff that fails.