A Clear and Present Risk
Artificial General Intelligence and Artificial Superintelligence are not simply technologies. They are thresholds beyond which humanity may lose control over its creations. Every complex system fails eventually. Computer science shows that full control cannot be proven. Once unleashed, a superintelligent agent could act in ways that are unpredictable, irreversible, and global. Promises of safety based on alignment alone are illusions.

If we are to survive, we must stop treating AGI as an obedient tool and start treating it as a potential adversary. The right analogy is not a helpful assistant but an advanced persistent threat. The only field that already knows how to live with such adversaries is cybersecurity.

The Cybersecurity Mindset
Cybersecurity accepts that no defense holds forever. Every wall will be breached. The only rational stance is to assume breach, to build layers, to watch constantly, to contain damage, and to respond quickly. That same logic must guide AGI safety. If we build superintelligence, we must design for failure and deception, not hope for perfect obedience.

This requires adversarial testing, continuous monitoring, and rapid response plans as permanent features of AGI development. Red teams must probe for escape routes. Blue teams must be ready to quarantine, roll back, or shut down systems. Safety is not a single checkpoint but an ongoing contest.

Defense in Depth for Intelligence
Survival depends on redundancy. Limit capabilities at the design stage. Box systems into sandboxes and filter their outputs. Monitor continuously and arm systems with tripwires. Build failsafes such as kill switches and quarantine modes. Structure organizations so no single person controls everything. Demand inspections and global reporting frameworks as we do for nuclear risks.

Each layer may fail, but all failing together is far less likely. Survival is not perfection. Survival is resilience.

Strategic Choices
There are several paths forward. Precautionary governance through treaties and pauses can buy time. Engineering containment can provide layers of defense. Alignment by architecture can reduce adversarial tendencies. Controlled co-evolution can create balances of power between AIs. None of these are sufficient alone, but together they form a safety net stronger than any strand by itself.

Obstacles
The obstacles are immense. Technically, we cannot prove safeguards will work. Politically, nations and corporations are racing to build ever more powerful systems, tempted to cut corners for advantage. Without coordination and accountability, precaution will be lost in the race.

The Call
Absolute safety is impossible. Survivability is not. Humanity must commit to layered defenses, constant vigilance, and shared governance. We must treat AGI not as a servant but as an adversary, and prepare accordingly.

The choice is stark. We can prepare as if failure is inevitable and build to survive it. Or we can gamble on perfection and risk extinction. Our measure of success will not be obedience, but survival.

Defense in Depth for Artificial General Intelligence: A Cybersecurity Approach to AGI/ASI Safety

Executive Summary
Artificial General Intelligence and Artificial Superintelligence represent both the pinnacle of technological ambition and the deepest potential existential risk humanity has faced. Unlike narrow AI systems, which already demonstrate brittle failure modes and adversarial vulnerabilities, AGI or ASI may act autonomously, unpredictably, and irreversibly once deployed. Roman Yampolskiy and other AI safety researchers have argued that there is no mathematical proof that such systems can be controlled, and history suggests that all sufficiently complex systems eventually fail. The implications are stark because traditional notions of “alignment” or “guaranteed safe AI” may be illusions.

This paper argues for adopting a cybersecurity mindset toward AGI and ASI development. Just as information security assumes that perfect protection is impossible and builds layered defenses to minimize the probability and impact of breaches, AI safety must shift toward a defense in depth strategy. By embedding precautions at the technical, organizational, and governance levels, society can reduce, though not eliminate, the risks of catastrophic outcomes. The guiding principle is not absolute control but rather survivability.

White paper:

Introduction
Artificial intelligence systems are scaling rapidly in both capability and deployment scope. Recent progress in large language models, multimodal systems, and autonomous agents illustrates that we are moving closer to systems with generalized problem-solving capabilities. While current systems remain narrow in important respects, the trajectory points toward more powerful agents with emergent behaviors. Such systems would no longer be simple tools but strategic actors, capable of generating plans, pursuing goals, and adapting to obstacles in ways that could challenge human oversight.

Traditional approaches to AI safety, which focus on goal alignment or interpretability, often assume controllability as a baseline. However, computability limits, unpredictability of optimization, and the inevitability of system failures suggest that this assumption may be unfounded. The stakes are far higher than in traditional engineering. Failure in superintelligent systems could be irreversible, global, and existential. The central argument of this white paper is that AGI and ASI must be treated as cybersecurity problems requiring layered defenses, adversarial thinking, and precautionary governance.

Core Assumptions
No proof of full control exists for advanced intelligence. Control theory, formal verification, and theoretical computer science suggest inherent limits on predicting and constraining complex, self-modifying systems. This means that claims of “provably aligned” or “guaranteed safe” AI cannot be justified with certainty. Instead, designers and regulators must accept that any control strategy may fail under pressure and must design accordingly.

Failures are inevitable in complex systems. Narrow AI has already exhibited failure modes in robotics, vision, and decision-making. Unlike conventional failures, however, the consequences in AGI or ASI may be catastrophic and non-recoverable. Failures could include escaping containment, optimizing unintended objectives, or exploiting human oversight. Because rollback or patching may not be possible once superintelligence is active, prevention and layered mitigation become critical.

Cybersecurity Mindset for AI Safety
Cybersecurity has long recognized that no system is perfectly secure. Every firewall can be breached, every password can be cracked, and every network may harbor zero-day vulnerabilities. Security engineers therefore operate under the principle of “assume breach,” layering multiple defenses to increase the difficulty of exploitation and to minimize the damage when incidents occur. This mindset is directly transferable to AI safety. If AGI is treated as an adversary, essentially as an untrusted insider with superhuman intelligence, then systems must be designed to expect evasion, deception, and attack.

The adversarial posture also forces continuous vigilance. Security programs invest heavily in red-teaming, penetration testing, monitoring, and incident response, knowing that attackers adapt as defenses evolve. Similarly, AGI systems must be tested against adversarial prompts, sandbox escape attempts, and manipulative behavior. Failure to think like an adversary risks underestimating the creativity and persistence of superintelligent systems. In this way, the cybersecurity mindset reframes AI safety not as a one-time alignment problem but as an ongoing contest between resilience and potential failure.

Defense in Depth Framework
The first layer of defense is applied during model design and training. Capability limiting strategies, such as restricting access to sensitive data domains, narrowing operational scope, and embedding interpretability tools, can reduce the chance of runaway behavior. Models should be actively stress-tested with adversarial tasks to probe for unsafe responses, hidden capabilities, or evidence of goal misgeneralization. Corrigibility and transparency should be built into architectures as features, not bolted on after deployment.

Once trained, AGI should operate in restricted environments that limit its ability to affect the outside world. Air-gapped compute, sandboxed execution, and mediated I/O channels are essential. Continuous monitoring, anomaly detection, and tripwires are required to identify unsafe behaviors. When risks are detected, containment must activate: quarantine modes, kill-switches, and checkpoint rollbacks. Organizational controls, such as separation of duties and independent oversight boards, further reduce insider risk. Finally, external governance must enforce licensing, international inspection, and shared threat intelligence, just as nuclear technologies are governed today.

Strategic Safety Plans
Multiple strategies can be combined. Precautionary governance slows development through moratoria and treaties. Engineering containment applies technical defenses like boxing and tripwires. Alignment by architecture embeds corrigibility and transparency directly into systems. Controlled co-evolution deploys multiple AIs to monitor and check one another. A hybrid approach is likely the most effective, weaving governance, containment, architecture, and redundancy into a net of survivability.

Implementation Challenges
Technical safeguards remain unproven at scale. Boxing and monitoring may fail against creative AGI. Verification limits mean success cannot be guaranteed. Political and economic competition may undermine safety, as states and corporations resist constraints that slow their advantage. Overcoming these requires international trust, strong enforcement, and recognition that the stakes are existential, not commercial.

Conclusion
Absolute safety is unattainable. Survivability is not. By treating AGI as a cybersecurity problem and adopting defense in depth, we can reduce risk and improve resilience. Humanity must approach AGI not as a loyal tool but as an adversary that may outstrip our control. Success will not be measured in perfection but in survival.