The Alignment Gap: Why We’re Fixing the Future While Ignoring Today’s AI Threats

AI is moving fast—and while much of the conversation focuses on distant risks like rogue superintelligences, the real danger is right here. We’re already using AI in hiring, lending, and law enforcement. These tools don’t just reflect our values—they amplify them, often in harmful ways. Biased training data leads to biased decisions, and that bias can be exploited. Meanwhile, attackers are using AI to find flaws faster, craft more convincing scams, and respond in real time to defenses. The biggest threat isn’t something that might happen in 2040—it’s what’s happening now, in the systems we use every day.

The obsession with “AI alignment”—ensuring future systems follow human values—has become a distraction. It’s a valuable idea, but it doesn’t stop bad actors from exploiting today’s tools. Defining what counts as “human values” is messy, subjective, and technically tough. No one knows if we can ever get it right. What we *do* know is that current AI systems have real, measurable flaws. We need to act—now—on the risks that exist in the systems we already run.

Current AI Risks: Real and Immediate

• Algorithmic Bias Amplification: AI learns from data, and if that data is biased—say, toward certain races, genders, or income levels—those biases get baked into decisions. We see this in hiring tools, credit scores, and criminal sentencing. A malicious actor could exploit this to target vulnerable groups or manipulate outcomes.
• Enhanced Cyberattack Capabilities: AI is now being used to automate scans, generate hyper-personalized phishing emails, and adapt to defenses in real time. This means attacks become smarter, faster, and harder to detect. Traditional defenses are no longer enough.
• Data Privacy Erosion: AI can analyze vast amounts of personal data to uncover hidden patterns. Facial recognition can enable mass surveillance. Analytics tools can de-anonymize private records. Without strong governance and safeguards, personal data becomes vulnerable to misuse by both hackers and developers who don’t follow best practices.

The real path forward isn’t waiting for perfect AI ethics. It’s about securing what we have today.

Explainable AI (XAI) Implementation: Use XAI to see how decisions are made. When a system gives a loan denial or rejects a job candidate, knowing *why* helps catch bias early and builds trust.

Continuous Monitoring & Threat Hunting: Set up systems that track unusual behavior—like sudden model shifts, strange data access, or AI tools being used for bad purposes. Catch threats before they cause damage.

Stronger Data Governance: Enforce clear policies on data use, access, and retention. Make sure developers and users understand the risks—and have tools to manage them.

We don’t need to predict the future of AI to protect ourselves. We just need to fix what’s already broken.