Every organization faces the ongoing challenge of managing risks while meeting its goals. This challenge takes on a new dimension as AI becomes more common in marketing tools and strategies. With the rapid evolution of AI-driven tools, closer scrutiny of these technologies' ethical implications, privacy concerns, and security vulnerabilities will inevitably follow. Risk-aware marketers and creators will be positioned to offer greater value to their organizations by proactively addressing these challenges and thinking like threat hunters, safeguarding their brands while navigating the new challenges using AI technology.
Strategic Risk Management and the Role of Threat Hunters
Risk is inherent in any operation, and strategic risk management smartly balances benefits and challenges to handle it effectively. This strategy doesn't eliminate risk but manages it better. Threat hunters are vital in this approach, as cybersecurity professionals who proactively search for and address hidden security threats before they cause harm. They proactively identify and mitigate threats before they grow, particularly novel ones that automated tools might miss. This helps bridge the gap between automated defenses and the ever-evolving nature of cyber threats.
Adopting the Threat Hunter's Mindset
Threat hunters are not just problem solvers but also strategic thinkers. They think like adversaries, understanding the latest tactics to uncover subtle signs of intrusion. They deeply understand their systems and are well-versed in adversaries' tactics. By applying this proactive and detail-oriented mindset to how we use AI, we can stay ahead of the curve—continuously learning and ready to adapt to new challenges posed by AI.
3 Key Threat Hunter Traits for Creators Using AI
1. The Systems Thinker
It's easy to focus on the amazing things AI can produce. But threat hunters – and you – need to understand how the system works to look for potential weaknesses. Even in large organizations, start with your sphere of influence. Consider how your AI tool interacts with your immediate work and team. Question potential issues, ask about the dependencies you can see, and anticipate your AI-powered work's impacts on your role and those you collaborate with directly.
Example Scenario: Your team uses an AI for sentiment analysis, which fails to recognize sarcasm and misleadingly boosts positive feedback scores. As a systems thinker, you delve into the AI's interpretation methods, uncovering this flaw and adjusting the tool's parameters to improve accuracy.
2. The Careful Analyst
Threat hunters excel at spotting subtle patterns. Today's attackers also use AI, making vigilance even more important. Creators can adopt a similar critical eye when working with AI. Look for unusual outputs, unexpected changes, or anything in the AI-generated content that seems "off." By training your eye to notice inconsistencies, you can catch early signs of bias, errors, or potential attacks hidden within the AI system.
Example Scenario: Your team uses an AI-powered tool to transcribe and analyze customer support calls. As a careful analyst, you notice that the AI consistently misinterprets specific technical terms, leading to inaccurate insights. You work to create a glossary of technical terms and use it to retrain the AI, improving the accuracy of the AI's transcription and analysis.
3. The Courageous Questioner
As creators, we boldly advocate for our ideas. We should do the same to question AI outputs and raise concerns when things don't seem right or meet our expectations. This involves investigating further and speaking up, even if it means challenging current practices. We should push back against potentially harmful or misleading content and advocate for changes in AI tools and processes.
Example Scenario: Your team is eager to use a new AI-powered tool, but you notice it wasn't designed with end-user input and doesn't integrate with some of your existing workflows. As a courageous questioner, you advocate for a more collaborative approach to AI implementation that considers the team's needs and ensures the tool enhances rather than disrupts their work.
Actionable Ways to Put Threat Hunter Thinking into Practice
Be a Data Detective
We already know the valuable role data plays in our work's success. The focus here is data integrity, usage, and impact on AI outputs.
Check your sources: Stay alert to its origins and consistency as data spreads across an organization. Data mainly from one demographic can bias AI, impacting accuracy and fairness. Always ask about data sources, check for consistency, report irregularities, engage in data management training, and adhere to protocols. Explore more here:
Keep data current: Always use the latest data. Outdated data can lead to inaccurate or irrelevant AI outputs. Explore more here: Data-Centric AI: AI Models Are Only as Good as Their Data Pipeline
Keep testing: Validate AI outputs against trusted sources. You already know that AI can produce inaccurate results. What's less known is a phenomenon called drift. Drift happens when an AI model's performance deteriorates over time due to underlying data or environment changes. If you observe any drift or inconsistencies in results, report them. Explore more here: What Are Data Drifts And How To Detect Them?
Adversaries can manipulate (poison) data to corrupt AI's results over time. Watch for drops in accuracy, unusual output biases, or seemingly random errors. Explore more here.Ask the Tough Questions to Keep AI in Check
AI's rapid development and complexity mean that not everything is fully understood, even by the people who create it. However, we shouldn't let this uncertainty stop us from seeking clear explanations and making informed decisions based on the information we have.
Understand your AI: When working with AI vendors, ask how their AI makes decisions, what data it uses, and how it's kept secure. Request documentation and case studies to gain a better understanding of the technology. Evaluate their readiness to perform pilots and proof-of-concept tests to ensure compatibility with your specific requirements. Here's some guidance, Select Your Generative AI Vendor.
Set clear AI guidelines: Make sure your organization adheres to clear rules and practices for responsible AI usage. If these guidelines are missing, take the initiative to establish them. Champion transparency, accountability, and ethical standards in your organization's AI practices. For support, consult The Imperatives of AI Governance.
Provide constructive feedback to AI vendors: We bring a unique perspective on how AI tools can be tailored to better serve our needs. Engage actively with AI vendors, offering feedback based on your experiences. Share insights about what works, pinpoint challenges, and suggest features or improvements.
Participate in AI learning communities: Join forums, workshops, and webinars focusing on AI technology. Participating in these communities boosts your understanding and allows you to contribute to the broader conversation about AI development and ethics.
AI-powered deepfakes are proliferating and can impersonate executives, undermining trust. Stay vigilant. Explore more here.Secure Your Creative Space
Integrating AI into creative workflows can unintentionally widen the attack surface.
Implement the "least privilege" principle: Restrict AI's access to data and systems to only what's necessary for functionality. Explore more about it here: What is the principle of least privilege?
Test and learn: Get involved in testing AI. This means testing the AI with varying types of content and scenarios it will encounter once fully deployed. Level up your understanding of pilots here: How to launch—and scale—a successful AI pilot project
Work together: From the start, ensure that legal, compliance, and security experts are involved in setting up AI tools to ensure everything is up to standard. Some general guidance is here: The Art of Creative Compliance: How to Ace the Balancing Act
Stay informed about rights for AI-generated content: Despite the uncertainty in intellectual property laws for AI-generated content, stay proactive with the latest information by setting up alerts for updates. Example here: The AI Industry Is Steaming Toward A Legal Iceberg
Adversaries target vendors to gain access to systems and data. This strategy can give attackers a high return, as a single breach might expose hundreds or thousands of your customers. Always follow vetting protocols. Explore more here.Be Ready for the Unexpected
Even with the best precautions, AI can still go off-script.
Plan for problems: Create an incident response plan that outlines roles, responsibilities, and procedures for handling AI irregularities or issues. Get some help here: AI incident response plans: Not just for security anymore
Encourage openness: Support a team culture where talking about worries and what might go wrong with AI is okay, making it easier to handle challenges together. Get some help here: Make It Safe for Employees to Speak Up — Especially in Risky Times
Stay informed: Keep up with AI and cybersecurity trends by following thought leaders and setting alerts for publications. Some resources to consider: AI Incident Database, NIST AI Risk Management Framework, and CISO SOS by Karen Worstell on Substack
81% of ransomware attacks analyzed target off-hours, risking greater damage due to slower responses. 43% were detected on either a Friday or Saturday. Know what to do. Explore more here. Adopting a threat hunter's mindset with AI means catching problems early and actively working to fix them. By applying the threat hunter's proactive, analytical, and courageous traits, you’re not just solving problems—you’re preventing them. This approach makes you both a strategic thinker and a proactive doer, going beyond merely keeping our systems secure and compliant. It positions you and your organization at the forefront of responsible and effective AI usage.
P.S. GPT-4o was announced. It gets sarcasm. See for yourself here.
About me
My deep appreciation for strategic risk management comes from bridging creativity and security, privacy and risk at Adobe, VMware, and Dell Technologies, where I created strategies to engage both creative minds and security experts. As this blog's editor, I focus on empowering creators to leverage AI while prioritizing security and compliance. I publish on LinkedIn and The Strategist Blog.
Referenced sources
Fairness and Bias in Artificial Intelligence, Viterbi School of Engineering
Data-Centric AI: AI Models Are Only as Good as Their Data Pipeline, Stanford University
What Are Data Drifts And How To Detect Them? Censius blog
Select Your Generative AI Vendor, Info-Tech Research Group
The Imperatives of AI Governance, The National Law Review
Security Predictions 2024, Splunk
What is the principle of least privilege? TechTarget
How to launch—and scale—a successful AI pilot project, CIO Magazine
AI incident response plans: Not just for security anymore, International Association of Privacy Professionals (IAAP)
Make It Safe for Employees to Speak Up — Especially in Risky Times, Harvard Business Review
Attack Dwell Times Fall but Threat Actors Are Moving Faster, Infosecurity Magazine
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