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"Some say we don't do engineering in Canada anymore, not real engineering, never mind AI engineering, As an engineering practice, it utilizes the engineering method embodied through design and prototyping Engineering Standards AI systems must be properly engineered, preferably by licensed professionals. The Call for AI Engineers The AI Engineering Body of Knowledge (AIENGBOK) The presentation culminated Raimund is a professional engineer with a bachelor’s degree in electrical / computer engineering from

As artificial intelligence continues to advance, AI engineers face a practical challenge – how to build This isn't merely a theoretical concern but a practical engineering problem that requires thoughtful Standard engineering approaches are effective here. 2. Whether formal regulation emerges or not, the engineering challenge remains clear. Rather than relying solely on static evidence, successful AI engineering requires ongoing observation

To use AI responsibly and effectively, we need to engineer it—with discipline, oversight, and purpose-built engineering—to ensure they are safe, reliable, and aligned with business and societal goals. Here are some key concepts behind engineering AI systems: 1.  Engineering for robustness means testing models under various scenarios, stress conditions, and edge , systems and model engineering, adaptive regulation, AI safety, and ethical design.

At its core, this is requirements engineering and system design work. Yet how many self-proclaimed "GRC engineers" can actually design systems and processes that deliver meaningful Simply calling yourself an engineer doesn't make you one. Other engineering disciplines have practice standards and licensing for good reason. It's time to establish formal practice standards for GRC engineering—education requirements, competency

What once required full engineering teams now happens through sophisticated prompt engineering and AI Where Traditional Engineering Still Matters Now this wave won't wash away all engineering domains equally  - Align your engineering practice from traditional engineering to AI system design; adopt AI engineering The engineers who adapted early secured the valuable remaining roles. practice focused on advancing AI engineering in Canada.

The document "Best Practices for Using Systems Engineering Standards (ISO/IEC/IEEE 15288, IEEE 15288.1 Systems engineering is the primary means for determining whether and how the challenge posed by a program We have observed that effective systems engineering processes and practices are essential for compliance If mission success depends on compliance success, make sure you incorporate systems engineering as a Lean Compliance offers an advanced program based on the principles of systems engineering along with

By Raimund Laqua, PMP, P.Eng., Founder and Chief Compliance Engineer at Lean Compliance I've spent 30 Raimund Laqua, PMP, P.Eng. is Founder and Chief Compliance Engineer at Lean Compliance Consulting, Inc

In this article we consider how both compliance and engineering have changed and why a new kind of engineering Professional engineering is defined in the Professional Engineers Act in Ontario, Canada where I practice Nature of a Compliance Engineer Compliance needs to be engineered. Systems Engineering (goal-seeking, purposeful, full stack systems) Computer Engineering (algorithms, We need to engineer our compliance not just audit our conformance. We need Compliance Engineers.

When it comes to decision-making, a common approach is to work forwards—to start from a problem and try to figure out the steps toward a solution. However, there is a lesser-known, yet profoundly effective mental model that turns this logic on its head: inversion. Coined by the German mathematician Carl Gustav Jacob Jacobi, the idea is encapsulated in the phrase "Invert, always invert" ("man muss immer umkehren"). The inversion mental model asks you to work backward from an undesirable outcome, rather than from a goal, to ensure success. If you can identify everything that could go wrong, you can avoid it. This approach is especially powerful in compliance, where the stakes of failure—whether in regulatory, ethical, or legal terms—are high. The Inversion Mindset: "Where I’m Going to Die, So I’ll Never Go There" Warren Buffett's longtime partner, Charlie Munger, succinctly captured the essence of inversion when he said: “All I want to know is where I’m going to die, so I’ll never go there.” This simple, almost humorous statement is deceptively profound. It suggests that avoiding failure is sometimes more effective than actively pursuing success. In compliance, the consequences of failure—fines, reputational damage, loss of trust, or even business collapse—are often more salient than the benefits of success. Therefore, by identifying potential failure points and systematically working to prevent them, businesses can enhance compliance outcomes. Inversion in Action: How to Apply it to Compliance Success Let’s explore how the inversion mental model can be used in compliance to achieve effective management systems and prevent costly missteps. 1. Identify the Worst-Case Scenario Start by asking the question: "What does complete compliance failure look like?" This step forces organizations to imagine worst-case scenarios such as regulatory fines, legal liabilities, fraud exposure, or reputational damage. By visualizing this end state, it becomes easier to define what exactly needs to be avoided. For example, a financial institution might define complete failure as being caught in a money laundering scandal. Once this is identified, the next step is to prevent it by putting stringent controls and risk measures in place. 2. Work Backwards to Pinpoint the Causes Once you have a clear picture of what failure looks like, work backward to identify the factors or decisions that could lead to that outcome. What behaviours, systems, or processes, if left unchecked, could contribute to compliance failure? If you think about a company being fined for non-compliance with anti-corruption laws, you would analyze what activities might trigger this failure. These could include lack of internal reporting, unclear policies on gifts or entertainment, or failure to conduct due diligence on third-party vendors. 3. Remove or Mitigate Potential Pitfalls Now that you’ve identified the causes of failure, the next step is to eliminate or mitigate these risk factors. This could mean revising internal policies, improving risk programs, conducting more control effectiveness assessments, or implementing predictive measures. To continue the financial institution example, once the risk of a money laundering scandal is identified, steps to mitigate it might include establishing more rigorous customer identification programs (KYC), improving transaction monitoring systems, and ensuring employees receive regular anti-money laundering (AML) training. 4. Create a Feedback Loop Inversion is not a one-time strategy but a continuous process. Regularly revisit the question: "Where could we fail?" This creates a feedback loop where potential issues are constantly identified and addressed. By staying vigilant, businesses can adapt to changing regulations and internal weaknesses that might arise. This loop is critical in industries like healthcare or finance, where regulatory landscapes are continually shifting. The ability to foresee potential failures before they happen gives businesses a proactive advantage in compliance management. Why Inversion Works So Well in Compliance Compliance success increases the probability of mission success. However, the reverse is almost always true: compliance failure leads to mission failure. The regulatory environment is complex, and failure to meet all your obligations can often arise from blind spots or unforeseen circumstances. The power of the inversion mental model is that it forces organizations to consider these blind spots and address them head-on. Moreover, effective compliance frameworks rely heavily on preventative controls—governance, programs, systems, and processes designed to reduce the likelihood of non-compliance. The inversion method aligns perfectly with this focus. By working backward from a failure scenario, companies can enhance the design and effectiveness of these functions. Avoiding Failure is Success in Compliance Achieving compliance success can be challenging for many to conceptualize. The inversion mental model offers a valuable approach by shifting focus to failure prevention. Rather than solely pursuing an idealized compliant state, this method emphasizes systematically identifying potential failure points and taking steps to avoid them. In essence, compliance success often stems from a thorough understanding and mitigation of possible pitfalls. In the end, Munger’s advice rings true: "All I want to know is where I’m going to die, so I’ll never go there." In compliance, this means that knowing where your business is vulnerable and proactively avoiding those pitfalls is often the key to long-term success.

As a compliance engineer, I've watched AI transform from research curiosity to world-changing technology Engineering Responsibility: A Practical Framework As compliance professionals, we must engineer responsibility As compliance engineers, we must move beyond checkbox exercises to become true stewards of responsible But by addressing them directly and engineering thoughtful oversight systems, we can shape an AI future Let's rise to this challenge by engineering responsibility into every aspect of AI development and deployment

The Future of Professional Engineering Over the years, I’ve seen a decline in professional engineering If anyone can perform engineering, then why do you need professional engineers? , network engineers, and even now prompt engineers, all of who do not have a license to practice. We have reduced professional engineering to the things that engineering does, and in the process, forgotten what engineering is.

One of the areas being overlooked is the role of Professional Engineers. Professional engineers bring a wealth of benefits to the table. Provincial regulators must act now to elevate engineering's role in the AI landscape. Professional engineering associations should develop and deliver training programs that equip engineers Provincial engineering regulators, in collaboration with professional engineering associations and stakeholders