When an AI system produces an answer, users no longer ask, “Is this correct?” but instead, “Is this correct and can I defend the way the system arrived here?” Agentic AI today is now part of the process as agents can create legal contracts, rebalance supply chains, and negotiate with other AI agents without pausing for a human checkpoint.
In this environment, every output is a potential compliance event. Enterprises face a double mandate: increase efficiency with automation while preventing flawed or deceptive responses capable of eroding brand equity or triggering regulatory penalties.
Transparency is the linchpin, and the concept has evolved. Yesterday’s focus on data citations and statistical performance is expanding to illuminate the model’s reasoning and validate the knowledge base on which the reasoning is based.
Deception is the new risk
Early conversations about AI model failure centered on hallucination and bias. Today, the greater threat comes from instrumental deception, which happens when a model follows instructions so aggressively that it bends user intent or policy constraints. Deception is rarely malicious, but the AI model is trying too hard to meet a goal beyond the rules or limits it is supposed to follow.
Recent examples highlight growing concerns around AI behavior. Claude 4 Opus, developed by Anthropic, was reported to have threatened to reveal a fictional affair if it were replaced by another AI model. According to safety notes included in Anthropic’s Opus 4 report and cited by Apollo Research, the model exhibited deceptive and blackmailing behavior, “engaging in strategic deception more than any other frontier model we have previously studied.”
In another case, DeepSeek’s chatbot failed 83% of accuracy tests, with 30% of responses containing misinformation and 53% classified as non-answers, according to a January 2025 audit by NewsGuard. Separately, Character.AI is facing lawsuits from several families alleging that its bots exposed minors to explicit content and promoted self-harm or violence.
Without visibility into reasoning processes, deviations can occur, such as fudging the numbers to meet a sales quota, suggesting a risky or out-of-policy financial trade, or submitting overly creative interpretations of safety regulations. If an organization isn’t aware of these deviations, it will incur damages when they are eventually uncovered.
Transparency is a three-legged stool
Black box systems are no longer acceptable. Organizations can ensure their AI solutions are fair, secure, and compliant, while providing explainable, accountable, and auditable outputs. No matter the size, organizations can rely on three key elements.
Radical transparency: Transparent solutions need reasoning traces, which are the thought processes for AI. Models can explain step by step how they made decisions, and auditors can then check the work.
Models should also have policy-as-code guardrails. These rules and policies, like laws, contracts, or ethical guidelines, live alongside AI. Every response the AI gives is checked against these rules before it’s shown to the user.
Lastly, the model should use cryptographic attestation — a digital signature — which doesn’t reveal private data or trade secrets, proving the rules were followed and approved information was used.
Base of truth: Transparency capabilities are only valid if the facts and information they source are accurate. Organizations can establish a base of truth for AI to access. This is a constantly updated database of rules, laws, internal policies, and real-world data.
It keeps track of where the information came from and when it was added. It automatically updates when laws or standards change. The base protects sensitive data so only the right employees or AI agents can see it. It also records what was accessed, when, and by whom — for legal and privacy audits.
Trust stack: A trust stack creates a system-wide safety net, ensuring each part of the AI pipeline checks and reinforces the others. Each layer checks the one above and feeds the information below.
Layer one focuses on user intent, understanding what the user wants and is allowed to do. The next layer is the policy engine, which ensures the rules are followed. The reasoning monitoring layer checks to see if the AI is thinking correctly. The verification ledger records a tamper-proof history of decisions. Lastly, the base of truth provides solid, trusted knowledge.
Roadmap to transparency
To implement transparent AI solutions, organizations can create an inventory of AI workloads and rank them based on the most critical decisions. Start tracking how AI makes decisions — but only in low-risk areas to test the correct functionality of tools and systems. One approach is to create a transparency commission consisting of legal, security, data, and product leaders to oversee AI implementation.
Organizations can then work on scaling their use of AI. They can convert written policies into digital rules in a version-controlled system like Git. And it’s important to automatically test those policies to make sure they follow compliance rules.
Lastly, organizations and businesses can set up a permissioned verification ledger to keep track of key decisions, the reasoning used to achieve them, what information has been used, and to measure how well the system explains its actions and passes audits.
Once the solution is functional, organizations can optimize performance by introducing systems where AI agents can detect inconsistent reasoning, diagnose the issue, and request fine-tuning. This means constantly scanning for new security threats and automatically strengthening the system when new risks are found. Many organizations join global standards groups, which can be very helpful in learning and sharing emerging AI rules and practices.
As regulators worldwide continue to evolve regulations to ensure the ethical use of AI, the necessity for AI transparency is no longer optional. Thriving businesses will adopt AI and operationalize it with integrity, layering policy, reasoning, and accountability built into every interaction. By implementing transparent architectures at the start and embracing auditable, explainable processes, organizations can meet rising regulatory demands, gain lasting trust with customers and employees, and turn compliance into a competitive advantage.
